Derive some facts of the negative binomial distribution

The previous post called The Negative Binomial Distribution gives a fairly comprehensive discussion of the negative binomial distribution. In this post, we fill in some of the details that are glossed over in that previous post. We derive the following points:

  • Discuss the several versions of the negative binomial distribution.
  • The negative binomial probabilities sum to one, i.e., the negative binomial probability function is a valid one.
  • Derive the moment generating function of the negative binomial distribution.
  • Derive the first and second moments and the variance of the negative binomial distribution.
  • An observation about independent sum of negative binomial distributions.

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Three versions

The negative binomial distribution has two parameters r and p, where r is a positive real number and 0<p<1. The first two versions arise from the case that r is a positive integer, which can be interpreted as the random experiment of a sequence of independent Bernoulli trials until the rth success (the trials have the same probability of success p). In this interpretation, there are two ways of recording the random experiment:

    X = the number of Bernoulli trials required to get the rth success.
    Y = the number of Bernoulli trials that end in failure before getting the rth success.

The other parameter p is the probability of success in each Bernoulli trial. The notation \binom{m}{n} is the binomial coefficient where m and n are non-negative integers and m \ge n is defined as:

    \displaystyle \binom{m}{n}=\frac{m!}{n! \ (m-n)!}=\frac{m(m-1) \cdots (m-(n-1))}{n!} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (0)

With this in mind, the following are the probability functions of the random variables X and Y.

    \displaystyle P(X=x)= \binom{x-1}{r-1} p^r (1-p)^{x-r} \ \ \ \ \ \ \ x=r,r+1,r+2,\cdots \ \ \ \ \ \ \ (1)

    \displaystyle P(Y=y)=\binom{y+r-1}{y} p^r (1-p)^y \ \ \ \ \ \ \ y=0,1,2,\cdots \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (2)

The thought process for (1) is that for the event X=x to happen, there can only be r-1 successes in the first x-1 trials and one additional success occurring in the last trial (the xth trial). The thought process for (2) is that for the event Y=y to happen, there are y+r trials (y failures and r successes). In the first y+r-1 trials, there can be only y failures (or equivalently r-1 successes). Note that X=Y+r. Thus knowing the mean of Y will derive the mean of X, a fact we will use below.

Instead of memorizing the probability functions (1) and (2), it is better to understand and remember the thought processes involved. Because of the natural interpretation of performing Bernoulli trials until the rth success, it is a good idea to introduce the negative binomial distribution via the distributions described by (1) and (2), i.e., the case where the parameter r is a positive integer. When r=1, the random experiment is a sequence of independent Bernoulli trials until the first success (this is called the geometric distribution).

Of course, (1) and (2) can also simply be used as counting distributions without any connection with a series of Bernoulli trials (e.g. used in an insurance context as the number of losses or claims arising from a group of insurance policies).

The binomial coefficient in (0) is defined when both numbers are non-negative integers and that the top one is greater than or equal to the bottom one. However, the rightmost term in (0) can be calculated even when the top number m is not a non-negative integer. Thus when m is any real number, the rightmost term (0) can be calculated provided that the bottom number n is a positive integer. For convenience we define \binom{m}{0}=1. With this in mind, the binomial coefficient \binom{m}{n} is defined for any real number m and any non-negative integer n.

The third version of the negative binomial distribution arises from the relaxation of the binomial coefficient \binom{m}{n} just discussed. With this in mind, the probability function in (2) can be defined for any positive real number r:

    \displaystyle P(Y=y)=\binom{y+r-1}{y} p^r (1-p)^y \ \ \ \ \ \ \ y=0,1,2,\cdots \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (3)

where \displaystyle \binom{y+r-1}{y}=\frac{(y+r-1)(y+r-2) \cdots (r+1)r}{y!}.

Of course when r is a positive integer, versions (2) and (3) are identical. When r is a positive real number but is not an integer, the distribution cannot be interpreted as the number of failures until the occurrence of rth success. Instead, it is used as a counting distribution.

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The probabilities sum to one

Do the probabilities in (1), (2) or (3) sum to one? For the interpretations of (1) and (2), is it possible to repeatedly perform Bernoulli trials and never get the rth success? For r=1, is it possible to never even get a success? In tossing a fair coin repeatedly, soon enough you will get a head and even if r is a large number, you will eventually get r number of heads. Here we wish to prove this fact mathematically.

To show that (1), (2) and (3) are indeed probability functions, we use a fact concerning Maclaurin’s series expansion of the function (1-x)^{-r}, a fact that is covered in a calculus course. In the following two results, r is a fixed positive real number and y is any non-negative integer:

    \displaystyle \binom{y+r-1}{y}=(-1)^y \ \binom{-r}{y} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (4)

    \displaystyle \sum \limits_{y=0}^\infty (-1)^y \ \binom{-r}{y} \ x^y=(1-x)^{-r} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ (5)

The result (4) is to rearrange the binomial coefficient in probability function (3) to another binomial coefficient with a negative number. This is why there is the word “negative” in negative binomial distribution. The result (5) is the Maclaurin’s series expansion for the function (1-x)^{-r}. We first derive these two facts and then use them to show that the negative binomial probabilities in (3) sum to one. The following derives (4).

    \displaystyle \begin{aligned} \binom{y+r-1}{y}&=\frac{(y+r-1)(y+r-2) \cdots (r+1)r}{y!} \\&=(-1)^y \ \frac{(-r)(-r-1) \cdots (-r-(y-1))}{y!} \\&=(-1)^y \ \binom{-r}{y}  \end{aligned}

To derive (5), let f(x)=(1-x)^{-r}. Based on a theorem that can be found in most calculus text, the function f(x) has the following Maclaurin’s series expansion (Maclaurin’s series is simply Taylor’s series with center = 0).

    \displaystyle (1-x)^{-r}=f(0)+f^{'}(0)x+\frac{f^{(2)}(0)}{2!}x^2+\frac{f^{(3)}(0)}{3!}x^3+\cdots + \frac{f^{(n)}(0)}{n!}x^n+\cdots

where -1<x<1. Now, filling in the derivatives f^{(n)}(0), we have the following derivation.

    \displaystyle \begin{aligned} (1-x)^{-r}&=1+rx+\frac{(r+1)r}{2!}x^2+\frac{(r+2)(r+1)r}{3!}x^3 \\& \ \ \ \ \ \ \ \ +\cdots+\frac{(r+y-1)(r+y-2) \cdots (r+1)r}{y!}x^y +\cdots \\&=1+(-1)^1 (-r)x+(-1)^2\frac{(-r)(-r-1)}{2!}x^2 \\& \ \ \ \ \ \ +(-1)^3 \frac{(-r)(-r-1)(-r-2)}{3!}x^3 +\cdots \\& \ \ \ \ \ \ +(-1)^y \frac{(-r)(-r-1) \cdots (-r-y+2)(-r-y+1)}{y!}x^y +\cdots  \\&=(-1)^0 \binom{-r}{0}x^0 +(-1)^1 \binom{-r}{1}x^1+(-1)^2 \binom{-r}{2}x^2 \\& \ \ \ \ \ \ +(-1)^3 \binom{-r}{3}x^3+\cdots +(-1)^y \binom{-r}{y}x^y+\cdots    \\&=\sum \limits_{y=0}^\infty (-1)^y \ \binom{-r}{y} \ x^y \end{aligned}

We can now show that the negative binomial probabilities in (3) sum to one. Let q=1-p.

    \displaystyle \begin{aligned} \sum \limits_{y=0}^\infty \binom{y+r-1}{y} \ p^r \ q^y &=p^r \ \sum \limits_{y=0}^\infty (-1)^y \ \binom{-r}{y} \ q^y \ \ \ \ \ \ \ \ \ \ \ \text{using } (4) \\&=p^r \ (1-q)^{-r} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{using } (5)\\&=p^r p^{-r} \\&=1 \end{aligned}

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The moment generating function

We now derive the moment generating function of the negative binomial distribution according to (3). The moment generation function is M(t)=E(e^{tY}) over all real numbers t for which M(t) is defined. The following derivation does the job.

    \displaystyle \begin{aligned} M(t)&=E(e^{tY}) \\&=\sum \limits_{y=0}^\infty \ e^{t y} \ \binom{y+r-1}{y} \ p^r \ (1-p)^y \\&=p^r \ \sum \limits_{y=0}^\infty  \ \binom{y+r-1}{y} \ [(1-p) e^t]^y \\&=p^r \ \sum \limits_{y=0}^\infty  \ (-1)^y \binom{-r}{y} \ [(1-p) e^t]^y \ \ \ \ \ \ \ \ \ \ \ \text{using } (4) \\&=p^r \ [1-(1-p) \ e^t]^{-r} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{using } (5) \\&=\frac{p^r}{[1-(1-p) \ e^t]^{r}}\end{aligned}

The above moment generating function works for the negative binomial distribution with respect to (3) and thus to (2). For the distribution in (1), note that X=Y+r. Thus E(e^{tX})=E(e^{t(Y+r)})=e^{tr} \ E(e^{tY}). The moment generating function of (1) is simply the above moment generating function multiplied by the factor e^{tr}. To summarize, the moment generating functions for the three versions are:

    \displaystyle M_X(t)=E[e^{tX}]=\frac{p^r \ e^{tr}}{[1-(1-p) \ e^t]^{r}} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{for } (1)

    \displaystyle M_Y(t)=E[e^{tY}]=\frac{p^r}{[1-(1-p) \ e^t]^{r}} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{for } (2) \text{ and } (3)

The domain of the moment generating function is the set of all t that for which M_X(t) or M_Y(t) is defined and is positive. Based on the form that it takes, we focus on making sure that 1-(1-p) \ e^t>0. This leads to the domain t<-\text{ln}(1-p).

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The mean and the variance

With the moment generating function derived in the above section, we can now focus on finding the moments of the negative binomial distribution. To find the moments, simply take the derivatives of the moment generating function and evaluate at t=0. For the distribution represented by the probability function in (3), we calculate the following:

    E(Y)=M_Y^{'}(0)

    E(Y^2)=M_Y^{(2)}(0)

    Var(Y)=E(Y^2)-E(Y)^2

After taking the first and second derivatives and evaluate at t=0, the first and the second moments are:

    \displaystyle E(Y)=r \ \frac{1-p}{p}

    \displaystyle E(Y^2)=\frac{r(1-p)[1+(1-p)]}{p^2}

The following derives the variance.

    \displaystyle \begin{aligned} Var(Y)&=E(Y^2)-E(Y)^2 \\&=\frac{r(1-p)[1+(1-p)]}{p^2}-\frac{(1-p)^2}{p^2} \\&=\frac{r(1-p)[1+r(1-p)-r(1-p)]}{p^2} \\&=\frac{r(1-p)}{p^2}  \end{aligned}

The above formula is the variance for the three versions (1), (2) and (3). Note that Var(Y)>E(Y). In contrast, the variance of the Poisson distribution is identical to its mean. Thus in the situation where the variance of observed data is greater than the sample mean, the negative binomial distribution should be a better fit than the Poisson distribution.

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The independent sum

There is an easy consequence that follows from the moment generating function derived above. The sum of several independent negative binomial distributions is also a negative binomial distribution. For example, suppose T_1,T_2, \cdots,T_n are independent negative binomial random variables (version (3)). Suppose each T_j has parameters r_j and p (the second parameter is identical). The moment generating function of the independent sum is the product of the individual moment generating functions. Thus the following is the moment generating function of T=T_1+\cdots+T_n.

    \displaystyle M_T(t)=E[e^{tT}]=\frac{p^g}{[1-(1-p) \ e^t]^{g}}

where g=r_1+\cdots+r_n. The moment generating function uniquely identifies the distribution. The above M_T(t) is that of a negative binomial distribution with parameters g and p according to (3).

A special case is that the sum of n independent geometric distributions is a negative binomial distribution with the r parameter being r=n. The following is the moment generating function of the sum W of n independent geometric distributions.

    \displaystyle M_W(t)=E[e^{tW}]=\frac{p^n}{[1-(1-p) \ e^t]^{n}}

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\copyright \ \text{2015 by Dan Ma}

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The Negative Binomial Distribution

A counting distribution is a discrete distribution with probabilities only on the nonnegative integers. Such distributions are important in insurance applications since they can be used to model the number of events such as losses to the insured or claims to the insurer. Though playing a prominent role in statistical theory, the Poisson distribution is not appropriate in all situations, since it requires that the mean and the variance are equaled. Thus the negative binomial distribution is an excellent alternative to the Poisson distribution, especially in the cases where the observed variance is greater than the observed mean.

The negative binomial distribution arises naturally from a probability experiment of performing a series of independent Bernoulli trials until the occurrence of the rth success where r is a positive integer. From this starting point, we discuss three ways to define the distribution. We then discuss several basic properties of the negative binomial distribution. Emphasis is placed on the close connection between the Poisson distribution and the negative binomial distribution.

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Definitions
We define three versions of the negative binomial distribution. The first two versions arise from the view point of performing a series of independent Bernoulli trials until the rth success where r is a positive integer. A Bernoulli trial is a probability experiment whose outcome is random such that there are two possible outcomes (success or failure).

Let X_1 be the number of Bernoulli trials required for the rth success to occur where r is a positive integer. Let p is the probability of success in each trial. The following is the probability function of X_1:

\displaystyle (1) \ \ \ \ \ P(X_1=x)= \binom{x-1}{r-1} p^r (1-p)^{x-r} \ \ \ \ \ \ \ x=r,r+1,r+2,\cdots

The idea for (1) is that for X_1=x to happen, there must be r-1 successes in the first x-1 trials and one additional success occurring in the last trial (the xth trial).

A more common version of the negative binomial distribution is the number of Bernoulli trials in excess of r in order to produce the rth success. In other words, we consider the number of failures before the occurrence of the rth success. Let X_2 be this random variable. The following is the probability function of X_2:

\displaystyle (2) \ \ \ \ \ P(X_2=x)=\binom{x+r-1}{x} p^r (1-p)^x \ \ \ \ \ \ \ x=0,1,2,\cdots

The idea for (2) is that there are x+r trials and in the first x+r-1 trials, there are x failures (or equivalently r-1 successes).

In both (1) and (2), the binomial coefficient is defined by

\displaystyle (3) \ \ \ \ \ \binom{y}{k}=\frac{y!}{k! \ (y-k)!}=\frac{y(y-1) \cdots (y-(k-1))}{k!}

where y is a positive integer and k is a nonnegative integer. However, the right-hand-side of (3) can be calculated even if y is not a positive integer. Thus the binomial coefficient \displaystyle \binom{y}{k} can be expanded to work for all real number y. However k must still be nonnegative integer.

\displaystyle (4) \ \ \ \ \ \binom{y}{k}=\frac{y(y-1) \cdots (y-(k-1))}{k!}

For convenience, we let \displaystyle \binom{y}{0}=1. When the real number y>k-1, the binomial coefficient in (4) can be expressed as:

\displaystyle (5) \ \ \ \ \ \binom{y}{k}=\frac{\Gamma(y+1)}{\Gamma(k+1) \Gamma(y-k+1)}

where \Gamma(\cdot) is the gamma function.

With the more relaxed notion of binomial coefficient, the probability function in (2) above can be defined for all real number r. Thus the general version of the negative binomial distribution has two parameters r and p, both real numbers, such that 0<p<1. The following is its probability function.

\displaystyle (6) \ \ \ \ \ P(X=x)=\binom{x+r-1}{x} p^r (1-p)^x \ \ \ \ \ \ \ x=0,1,2,\cdots

Whenever r in (6) is a real number that is not a positive integer, the interpretation of counting the number of failures until the occurrence of the rth success is no longer important. Instead we can think of it simply as a count distribution.

The following alternative parametrization of the negative binomial distribution is also useful.

\displaystyle (6a) \ \ \ \ \ P(X=x)=\binom{x+r-1}{x} \biggl(\frac{\alpha}{\alpha+1}\biggr)^r \biggl(\frac{1}{\alpha+1}\biggr)^x \ \ \ \ \ \ \ x=0,1,2,\cdots

The parameters in this alternative parametrization are r and \alpha>0. Clearly, the ratio \frac{\alpha}{\alpha+1} takes the place of p in (6). Unless stated otherwise, we use the parametrization of (6).
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What is negative about the negative binomial distribution?
What is negative about this distribution? What is binomial about this distribution? The name is suggested by the fact that the binomial coefficient in (6) can be rearranged as follows:

\displaystyle \begin{aligned}(7) \ \ \ \ \ \binom{x+r-1}{x}&=\frac{(x+r-1)(x+r-2) \cdots r}{x!} \\&=(-1)^x \frac{(-r-(x-1))(-r-(x-2)) \cdots (-r)}{x!} \\&=(-1)^x \frac{(-r)(-r-1) \cdots (-r-(x-1))}{x!} \\&=(-1)^x \binom{-r}{x} \end{aligned}

The calculation in (7) can be used to verify that (6) is indeed a probability function, that is, all the probabilities sum to 1.

\displaystyle \begin{aligned}(8) \ \ \ \ \ 1&=p^r p^{-r}\\&=p^r (1-q)^{-r} \\&=p^r \sum \limits_{x=0}^\infty \binom{-r}{x} (-q)^x \ \ \ \ \ \ \ \ (8.1) \\&=p^r \sum \limits_{x=0}^\infty (-1)^x \binom{-r}{x} q^x \\&=\sum \limits_{x=0}^\infty \binom{x+r-1}{x} p^r q^x \end{aligned}

In (8), we take q=1-p. The step (8.1) above uses the following formula known as the Newton’s binomial formula.

\displaystyle (9) \ \ \ \ \ (1+t)^w=\sum \limits_{k=0}^\infty \binom{w}{k} t^k

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The Generating Function
By definition, the following is the generating function of the negative binomial distribution, using :

\displaystyle (10) \ \ \ \ \ g(z)=\sum \limits_{x=0}^\infty \binom{r+x-1}{x} p^r q^x z^x

where q=1-p. Using a similar calculation as in (8), the generating function can be simplified as:

\displaystyle (11) \ \ \ \ \ g(z)=p^r (1-q z)^{-r}=\frac{p^r}{(1-q z)^r}=\frac{p^r}{(1-(1-p) z)^r}; \ \ \ \ \ z<\frac{1}{1-p}

As a result, the moment generating function of the negative binomial distribution is:

\displaystyle (12) \ \ \ \ \ M(t)=\frac{p^r}{(1-(1-p) e^t)^r}; \ \ \ \ \ \ \ t<-ln(1-p)

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Independent Sum

One useful property of the negative binomial distribution is that the independent sum of negative binomial random variables, all with the same parameter p, also has a negative binomial distribution. Let Y=Y_1+Y_2+\cdots+Y_n be an independent sum such that each X_i has a negative binomial distribution with parameters r_i and p. Then the sum Y=Y_1+Y_2+\cdots+Y_n has a negative binomial distribution with parameters r=r_1+\cdots+r_n and p.

Note that the generating function of an independent sum is the product of the individual generating functions. The following shows that the product of the individual generating functions is of the same form as (11), thus proving the above assertion.

\displaystyle (13) \ \ \ \ \ h(z)=\frac{p^{\sum \limits_{i=1}^n r_i}}{(1-(1-p) z)^{\sum \limits_{i=1}^n r_i}}
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Mean and Variance
The mean and variance can be obtained from the generating function. From E(X)=g'(1) and E(X^2)=g'(1)+g^{(2)}(1), we have:

\displaystyle (14) \ \ \ \ \ E(X)=\frac{r(1-p)}{p} \ \ \ \ \ \ \ \ \ \ \ \ \ Var(X)=\frac{r(1-p)}{p^2}

Note that Var(X)=\frac{1}{p} E(X)>E(X). Thus when the sample data suggest that the variance is greater than the mean, the negative binomial distribution is an excellent alternative to the Poisson distribution. For example, suppose that the sample mean and the sample variance are 3.6 and 7.1. In exploring the possibility of fitting the data using the negative binomial distribution, we would be interested in the negative binomial distribution with this mean and variance. Then plugging these into (14) produces the negative binomial distribution with r=3.7 and p=0.507.
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The Poisson-Gamma Mixture
One important application of the negative binomial distribution is that it is a mixture of a family of Poisson distributions with Gamma mixing weights. Thus the negative binomial distribution can be viewed as a generalization of the Poisson distribution. The negative binomial distribution can be viewed as a Poisson distribution where the Poisson parameter is itself a random variable, distributed according to a Gamma distribution. Thus the negative binomial distribution is known as a Poisson-Gamma mixture.

In an insurance application, the negative binomial distribution can be used as a model for claim frequency when the risks are not homogeneous. Let N has a Poisson distribution with parameter \theta, which can be interpreted as the number of claims in a fixed period of time from an insured in a large pool of insureds. There is uncertainty in the parameter \theta, reflecting the risk characteristic of the insured. Some insureds are poor risks (with large \theta) and some are good risks (with small \theta). Thus the parameter \theta should be regarded as a random variable \Theta. The following is the conditional distribution of N (conditional on \Theta=\theta):

\displaystyle (15) \ \ \ \ \ P(N=n \lvert \Theta=\theta)=\frac{e^{-\theta} \ \theta^n}{n!} \ \ \ \ \ \ \ \ \ \ n=0,1,2,\cdots

Suppose that \Theta has a Gamma distribution with scale parameter \alpha and shape parameter \beta. The following is the probability density function of \Theta.

\displaystyle (16) \ \ \ \ \ g(\theta)=\frac{\alpha^\beta}{\Gamma(\beta)} \theta^{\beta-1} e^{-\alpha \theta} \ \ \ \ \ \ \ \ \ \ \theta>0

Then the joint density of N and \Theta is:

\displaystyle (17) \ \ \ \ \ P(N=n \lvert \Theta=\theta) \ g(\theta)=\frac{e^{-\theta} \ \theta^n}{n!} \ \frac{\alpha^\beta}{\Gamma(\beta)} \theta^{\beta-1} e^{-\alpha \theta}

The unconditional distribution of N is obtained by summing out \theta in (17).

\displaystyle \begin{aligned}(18) \ \ \ \ \ P(N=n)&=\int_0^\infty P(N=n \lvert \Theta=\theta) \ g(\theta) \ d \theta \\&=\int_0^\infty \frac{e^{-\theta} \ \theta^n}{n!} \ \frac{\alpha^\beta}{\Gamma(\beta)} \ \theta^{\beta-1} \ e^{-\alpha \theta} \ d \theta \\&=\int_0^\infty \frac{\alpha^\beta}{n! \ \Gamma(\beta)} \ \theta^{n+\beta-1} \ e^{-(\alpha+1) \theta} d \theta \\&=\frac{\alpha^\beta}{n! \ \Gamma(\beta)} \ \frac{\Gamma(n+\beta)}{(\alpha+1)^{n+\beta}} \int_0^\infty \frac{(\alpha+1)^{n+\beta}}{\Gamma(n+\beta)} \theta^{n+\beta-1} \ e^{-(\alpha+1) \theta} d \theta \\&=\frac{\alpha^\beta}{n! \ \Gamma(\beta)} \ \frac{\Gamma(n+\beta)}{(\alpha+1)^{n+\beta}} \\&=\frac{\Gamma(n+\beta)}{\Gamma(n+1) \ \Gamma(\beta)} \ \biggl( \frac{\alpha}{\alpha+1}\biggr)^\beta \ \biggl(\frac{1}{\alpha+1}\biggr)^n \\&=\binom{n+\beta-1}{n} \ \biggl( \frac{\alpha}{\alpha+1}\biggr)^\beta \ \biggl(\frac{1}{\alpha+1}\biggr)^n \ \ \ \ \ \ \ \ \ n=0,1,2,\cdots \end{aligned}

Note that the integral in the fourth step in (18) is 1.0 since the integrand is the pdf of a Gamma distribution. The above probability function is that of a negative binomial distribution. It is of the same form as (6a). Equivalently, it is also of the form (6) with parameter r=\beta and p=\frac{\alpha}{\alpha+1}.

The variance of the negative binomial distribution is greater than the mean. In a Poisson distribution, the mean equals the variance. Thus the unconditional distribution of N is more dispersed than its conditional distributions. This is a characteristic of mixture distributions. The uncertainty in the parameter variable \Theta has the effect of increasing the unconditional variance of the mixture distribution of N. The variance of a mixture distribution has two components, the weighted average of the conditional variances and the variance of the conditional means. The second component represents the additional variance introduced by the uncertainty in the parameter \Theta (see The variance of a mixture).

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The Poisson Distribution as Limit of Negative Binomial
There is another connection to the Poisson distribution, that is, the Poisson distribution is a limiting case of the negative binomial distribution. We show that the generating function of the Poisson distribution can be obtained by taking the limit of the negative binomial generating function as r \rightarrow \infty. Interestingly, the Poisson distribution is also the limit of the binomial distribution.

In this section, we use the negative binomial parametrization of (6a). By replacing \frac{\alpha}{\alpha+1} for p, the following are the mean, variance, and the generating function for the probability function in (6a):

\displaystyle \begin{aligned}(19) \ \ \ \ \ \ &E(X)=\frac{r}{\alpha} \\&\text{ }\\&Var(X)=\frac{\alpha+1}{\alpha} \ \frac{r}{\alpha}=\frac{r(\alpha+1)}{\alpha^2} \\&\text{ } \\&g(z)=\frac{1}{[1-\frac{1}{\alpha}(z-1)]^r} \ \ \ \ \ \ \ z<\alpha+1 \end{aligned}

Let r goes to infinity and \displaystyle \frac{1}{\alpha} goes to zero and at the same time keeping their product constant. Thus \displaystyle \mu=\frac{r}{\alpha} is constant (this is the mean of the negative binomial distribution). We show the following:

\displaystyle (20) \ \ \ \ \ \lim \limits_{r \rightarrow \infty} [1-\frac{\mu}{r}(z-1)]^{-r}=e^{\mu (z-1)}

The right-hand side of (20) is the generating function of the Poisson distribution with mean \mu. The generating function in the left-hand side is that of a negative binomial distribution with mean \displaystyle \mu=\frac{r}{\alpha}. The following is the derivation of (20).

\displaystyle \begin{aligned}(21) \ \ \ \ \ \lim \limits_{r \rightarrow \infty} [1-\frac{\mu}{r}(z-1)]^{-r}&=\lim \limits_{r \rightarrow \infty} e^{\displaystyle \biggl(ln[1-\frac{\mu}{r}(z-1)]^{-r}\biggr)} \\&=\lim \limits_{r \rightarrow \infty} e^{\displaystyle \biggl(-r \ ln[1-\frac{\mu}{r}(z-1)]\biggr)} \\&=e^{\displaystyle \biggl(\lim \limits_{r \rightarrow \infty} -r \ ln[1-\frac{\mu}{r}(z-1)]\biggr)} \end{aligned}

We now focus on the limit in the exponent.

\displaystyle \begin{aligned}(22) \ \ \ \ \ \lim \limits_{r \rightarrow \infty} -r \ ln[1-\frac{\mu}{r}(z-1)]&=\lim \limits_{r \rightarrow \infty} \frac{ln(1-\frac{\mu}{r} (z-1))^{-1}}{r^{-1}} \\&=\lim \limits_{r \rightarrow \infty} \frac{(1-\frac{\mu}{r} (z-1)) \ \mu (z-1) r^{-2}}{r^{-2}} \\&=\mu (z-1) \end{aligned}

The middle step in (22) uses the L’Hopital’s Rule. The result in (20) is obtained by combining (21) and (22).

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Reference

  1. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004

The Poisson Distribution

Let \alpha be a positive constant. Consider the following probability distribution:

\displaystyle (1) \ \ \ \ \ P(X=j)=\frac{e^{-\alpha} \alpha^j}{j!} \ \ \ \ \ j=0,1,2,\cdots

The above distribution is said to be a Poisson distribution with parameter \alpha. The Poisson distribution is usually used to model the random number of events occurring in a fixed time interval. As will be shown below, E(X)=\alpha. Thus the parameter \alpha is the rate of occurrence of the random events; it indicates on average how many events occur per unit of time. Examples of random events that may be modeled by the Poisson distribution include the number of alpha particles emitted by a radioactive substance counted in a prescribed area during a fixed period of time, the number of auto accidents in a fixed period of time or the number of losses arising from a group of insureds during a policy period.

Each of the above examples can be thought of as a process that generates a number of arrivals or changes in a fixed period of time. If such a counting process leads to a Poisson distribution, then the process is said to be a Poisson process.

We now discuss some basic properties of the Poisson distribution. Using the Taylor series expansion of e^{\alpha}, the following shows that (1) is indeed a probability distribution.

\displaystyle . \ \ \ \ \ \ \ \sum \limits_{j=0}^\infty \frac{e^{-\alpha} \alpha^j}{j!}=e^{-\alpha} \sum \limits_{j=0}^\infty \frac{\alpha^j}{j!}=e^{-\alpha} e^{\alpha}=1

The generating function of the Poisson distribution is g(z)=e^{\alpha (z-1)} (see The generating function). The mean and variance can be calculated using the generating function.

\displaystyle \begin{aligned}(2) \ \ \ \ \ &E(X)=g'(1)=\alpha \\&\text{ } \\&E[X(X-1)]=g^{(2)}(1)=\alpha^2 \\&\text{ } \\&Var(X)=E[X(X-1)]+E(X)-E(X)^2=\alpha^2+\alpha-\alpha^2=\alpha \end{aligned}

The Poisson distribution can also be interpreted as an approximation to the binomial distribution. It is well known that the Poisson distribution is the limiting case of binomial distributions (see [1] or this post).

\displaystyle (3) \ \ \ \ \ \lim \limits_{n \rightarrow \infty} \binom{n}{j} \biggl(\frac{\alpha}{n}\biggr)^j \biggl(1-\frac{\alpha}{n}\biggr)^{n-j}=\frac{e^{-\alpha} \alpha^j}{j!}

One application of (3) is that we can use Poisson probabilities to approximate Binomial probabilities. The approximation is reasonably good when the number of trials n in a binomial distribution is large and the probability of success p is small. The binomial mean is n p and the variance is n p (1-p). When p is small, 1-p is close to 1 and the binomial variance is approximately np \approx n p (1-p). Whenever the mean of a discrete distribution is approximately equaled to the mean, the Poisson approximation is quite good. As a rule of thumb, we can use Poisson to approximate binomial if n \le 100 and p \le 0.01.

As an example, we use the Poisson distribution to estimate the probability that at most 1 person out of 1000 will have a birthday on the New Year Day. Let n=1000 and p=365^{-1}. So we use the Poisson distribution with \alpha=1000 \times 365^{-1}. The following is an estimate using the Poisson distribution.

\displaystyle . \ \ \ \ \ \ \ P(X \le 1)=e^{-\alpha}+\alpha e^{-\alpha}=(1+\alpha) e^{-\alpha}=0.2415

Another useful property is that the independent sum of Poisson distributions also has a Poisson distribution. Specifically, if each X_i has a Poisson distribution with parameter \alpha_i, then the independent sum X=X_1+\cdots+X_n has a Poisson distribution with parameter \alpha=\alpha_1+\cdots+\alpha_n. One way to see this is that the product of Poisson generating functions has the same general form as g(z)=e^{\alpha (z-1)} (see The generating function). One interpretation of this property is that when merging several arrival processes, each of which follow a Poisson distribution, the result is still a Poisson distribution.

For example, suppose that in an airline ticket counter, the arrival of first class customers follows a Poisson process with a mean arrival rate of 8 per 15 minutes and the arrival of customers flying coach follows a Poisson distribution with a mean rate of 12 per 15 minutes. Then the arrival of customers of either types has a Poisson distribution with a mean rate of 20 per 15 minutes or 80 per hour.

A Poisson distribution with a large mean can be thought of as an independent sum of Poisson distributions. For example, a Poisson distribution with a mean of 50 is the independent sum of 50 Poisson distributions each with mean 1. Because of the central limit theorem, when the mean is large, we can approximate the Poisson using the normal distribution.

In addition to merging several Poisson distributions into one combined Poisson distribution, we can also split a Poisson into several Poisson distributions. For example, suppose that a stream of customers arrives according to a Poisson distribution with parameter \alpha and each customer can be classified into one of two types (e.g. no purchase vs. purchase) with probabilities p_1 and p_2, respectively. Then the number of “no purchase” customers and the number of “purchase” customers are independent Poisson random variables with parameters \alpha p_1 and \alpha p_2, respectively. For more details on the splitting of Poisson, see Splitting a Poisson Distribution.

Reference

  1. Feller W. An Introduction to Probability Theory and Its Applications, Third Edition, John Wiley & Sons, New York, 1968

The hazard rate function

In this post, we introduce the hazard rate function using the notions of non-homogeneous Poisson process.

In a Poisson process, changes occur at a constant rate \lambda per unit time. Suppose that we interpret the changes in a Poisson process from a mortality point of view, i.e. a change in the Poisson process mean a termination of a system, be it biological or manufactured, and this Poisson process counts the number of of terminations as they occur. Then the rate of change \lambda is interpreted as a hazard rate (or failure rate or force of mortality). With a constant force of mortality, the time until the next change is exponentially distributed. In this post, we discuss the hazard rate function in a more general setting. The process that counts of the number of terminations will no longer have a constant hazard rate, and instead will have a hazard rate function \lambda(t), a function of time t. Such a counting process is called a non-homogeneous Poisson process. We discuss the survival probability models (the time to the next termination) associated with a non-homogeneous Poisson process. We then discuss several important examples of survival probability models, including the Weibull distribution, the Gompertz distribution and the model based on the Makeham’s law. We aso comment briefly the connection between the hazard rate function and the tail weight of a distribution.

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The Poisson Process
We start with the three postulates of a Poisson process. Consider an experiment in which the occurrences of a certain type of events are counted during a given time interval. We call the occurrence of the type of events in question a change. We assume the following three conditions:

  1. The numbers of changes occurring in nonoverlapping intervals are independent.
  2. The probability of two or more changes taking place in a sufficiently small interval is essentially zero.
  3. The probability of exactly one change in the short interval (t,t+\delta) is approximately \lambda \delta where \delta is sufficiently small and \lambda is a positive constant.

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When we interpret the Poisson process in a mortality point of view, the constant \lambda is a hazard rate (or force of mortality), which can be interpreted as the rate of failure at the next instant given that the life has survived to time t. With a constant force of mortality, the survival model (the time until the next termination) has an exponential distribution with mean \frac{1}{\lambda}. We wish to relax the constant force of mortality assumption by making \lambda a function of t instead. The remainder of this post is based on the non-homogeneous Poisson process defined below.

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The Non-Homogeneous Poisson Process
We modifiy condition 3 above by making \lambda(t) a function of t. We have the following modified counting process.

  1. The numbers of changes occurring in nonoverlapping intervals are independent.
  2. The probability of two or more changes taking place in a sufficiently small interval is essentially zero.
  3. The probability of exactly one change in the short interval (t,t+\delta) is approximately \lambda(t) \delta where \delta is sufficiently small and \lambda(t) is a nonnegative function of t.

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We focus on the survival model aspect of such counting processes. Such process can be interpreted as models for the number of changes occurred in a time interval where a change means “termination” or ‘failure” of a system under consideration. The rate of change function \lambda(t) indicated in condition 3 is called the hazard rate function. It is also called the failure rate function in reliability engineering and the force of mortality in life contingency theory.

Based on condition 3 in the non-homogeneous Poisson process, the hazard rate function \lambda(t) can be interpreted as the rate of failure at the next instant given that the life has survived to time t.

Two random variables naturally arise from a non-homogeneous Poisson process are described here. One is the discrete variable N_t, defined as the number of changes in the time interval (0,t). The other is the continuous random variable T, defined as the time until the occurrence of the first change. The probability distribution of T is called a survival model. The following is the link between N_t and T.

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\displaystyle \begin{aligned}(1) \ \ \ \ \ \ \ \ \ &P[T > t]=P[N_t=0] \end{aligned}

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Note that P[T > t] is the probability that the next change occurs after time t. This means that there is no change within the interval (0,t). We have the following theorems.

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Theorem 1.
Let \displaystyle \Lambda(t)=\int_{0}^{t} \lambda(y) dy. Then e^{-\Lambda(t)} is the probability that there is no change in the interval (0,t). That is, \displaystyle P[N_t=0]=e^{-\Lambda(t)}.

Proof. We are interested in finding the probability of zero changes in the interval (0,y+\delta). By condition 1, the numbers of changes in the nonoverlapping intervals (0,y) and (y,y+\delta) are independent. Thus we have:

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\displaystyle (2) \ \ \ \ \ \ \ \ P[N_{y+\delta}=0] \approx P[N_y=0] \times [1-\lambda(y) \delta]

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Note that by condition 3, the probability of exactly one change in the small interval (y,y+\delta) is \lambda(y) \delta. Thus [1-\lambda(y) \delta] is the probability of no change in the interval (y,y+\delta). Continuing with equation (2), we have the following derivation:

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\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\frac{P[N_{y+\delta}=0] - P[N_y=0]}{\delta} \approx -\lambda(y) P[N_y=0] \\&\text{ } \\&\frac{d}{dy} P[N_y=0]=-\lambda(y) P[N_y=0] \\&\text{ } \\&\frac{\frac{d}{dy} P[N_y=0]}{P[N_y=0]}=-\lambda(y) \\&\text{ } \\&\int_0^{t} \frac{\frac{d}{dy} P[N_y=0]}{P[N_y=0]} dy=-\int_0^{t} \lambda(y)dy \end{aligned}

\text{ }

Evaluating the integral on the left hand side with the boundary condition of P[N_0=0]=1 produces the following results:

\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &ln P[N_t=0]=-\int_0^{t} \lambda(y)dy \\&\text{ } \\&P[N_t=0]=\displaystyle e^{\displaystyle -\int_0^{t} \lambda(y)dy} \end{aligned}

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Theorem 2
As discussed above, let T be the length of the interval that is required to observe the first change. Then the following are the distribution function, survival function and pdf of T:

\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &F_T(t)=\displaystyle 1-e^{-\int_0^t \lambda(y) dy} \\&\text{ } \\&S_T(t)=\displaystyle e^{-\int_0^t \lambda(y) dy} \\&\text{ } \\&f_T(t)=\displaystyle \lambda(t) e^{-\int_0^t \lambda(y) dy} \end{aligned}

Proof. In Theorem 1, we derive the probability P[N_y=0] for the discrete variable N_y derived from the non-homogeneous Poisson process. We now consider the continuous random variable T, the time until the first change, which is related to N_t by (1). Thus S_T(t)=P[T > t]=P[N_t=0]=e^{-\int_0^t \lambda(y) dy}. The distribution function and density function can be derived accordingly.

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Theorem 3
The hazard rate function \lambda(t) is equivalent to each of the following:

\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\lambda(t)=\frac{f_T(t)}{1-F_T(t)} \\&\text{ } \\&\lambda(t)=\frac{-S_T^{'}(t)}{S_T(t)} \end{aligned}

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Remark
Theorem 1 and Theorem 2 show that in a non-homogeneous Poisson process as described above, the hazard rate function \lambda(t) completely specifies the probability distribution of the survival model T (the time until the first change) . Once the rate of change function \lambda(t) is known in the non-homogeneous Poisson process, we can use it to generate the survival function S_T(t). All of the examples of survival models given below are derived by assuming the functional form of the hazard rate function. The result in Theorem 2 holds even outside the context of a non-homogeneous Poisson process, that is, given the hazard rate function \lambda(t), we can derive the three distributional items S_T(t), F_T(t), f_T(t).

The ratio in Theorem 3 indicates that the probability distribution determines the hazard rate function. In fact, the ratio in Theorem 3 is the usual definition of the hazard rate function. That is, the hazard rate function can be defined as the ratio of the density and the survival function (one minus the cdf). With this definition, we can also recover the survival function. Whenever \displaystyle \lambda(x)=\frac{f_X(x)}{1-F_X(x)}, we can derive:

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\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &S_X(x)=\displaystyle e^{-\int_0^t \lambda(y) dy} \end{aligned}

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As indicated above, the hazard rate function can be interpreted as the failure rate at time t given that the life in question has survived to time t. It is the rate of failure at the next instant given that the life or system being studied has survived up to time t.

It is interesting to note that the function \Lambda(t)=\int_0^t \lambda(y) dy defined in Theorem 1 is called the cumulative hazard rate function. Thus the cumulative hazard rate function is an alternative way of representing the hazard rate function (see the discussion on Weibull distribution below).

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Examples of Survival Models

–Exponential Distribution–
In many applications, especially those for biological organisms and mechanical systems that wear out over time, the hazard rate \lambda(t) is an increasing function of t. In other words, the older the life in question (the larger the t), the higher chance of failure at the next instant. For humans, the probability of a 85 years old dying in the next year is clearly higher than for a 20 years old. In a Poisson process, the rate of change \lambda(t)=\lambda indicated in condition 3 is a constant. As a result, the time T until the first change derived in Theorem 2 has an exponential distribution with parameter \lambda. In terms of mortality study or reliability study of machines that wear out over time, this is not a realistic model. However, if the mortality or failure is caused by random external events, this could be an appropriate model.

–Weibull Distribution–
This distribution is an excellent model choice for describing the life of manufactured objects. It is defined by the following cumulative hazard rate function:

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\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\Lambda(t)=\biggl(\frac{t}{\beta}\biggr)^{\alpha} \end{aligned} where \alpha > 0 and \beta>0

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As a result, the hazard rate function, the density function and the survival function for the lifetime distribution are:

\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\lambda(t)=\frac{\alpha}{\beta} \biggl(\frac{t}{\beta}\biggr)^{\alpha-1} \\&\text{ } \\&f_T(t)=\frac{\alpha}{\beta} \biggl(\frac{t}{\beta}\biggr)^{\alpha-1} \displaystyle e^{\displaystyle -\biggl[\frac{t}{\beta}\biggr]^{\alpha}} \\&\text{ } \\&S_T(t)=\displaystyle e^{\displaystyle -\biggl[\frac{t}{\beta}\biggr]^{\alpha}} \end{aligned}

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The parameter \alpha is the shape parameter and \beta is the scale parameter. When \alpha=1, the hazard rate becomes a constant and the Weibull distribution becomes an exponential distribution.

When the parameter \alpha<1, the failure rate decreases over time. One interpretation is that most of the defective items fail early on in the life cycle. Once they they are removed from the population, failure rate decreases over time.

When the parameter 1<\alpha, the failure rate increases with time. This is a good candidate for a model to describe the lifetime of machines or systems that wear out over time.

–The Gompertz Distribution–
The Gompertz law states that the force of mortality or failure rate increases exponentially over time. It describe human mortality quite accurately. The following is the hazard rate function:

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\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\lambda(t)=\alpha e^{\beta t} \end{aligned} where \alpha>0 and \beta>0.

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The following are the cumulative hazard rate function as well as the survival function, distribution function and the pdf of the lifetime distribution T.

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\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\Lambda(t)=\int_0^t \alpha e^{\beta y} dy=\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta} \\&\text{ } \\&S_T(t)=\displaystyle e^{\displaystyle -\biggl(\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta}\biggr)} \\&\text{ } \\&F_T(t)=\displaystyle 1-e^{\displaystyle -\biggl(\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta}\biggr)} \\&\text{ } \\&f_T(t)=\displaystyle \alpha \ e^{\beta t} \ e^{\displaystyle -\biggl(\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta}\biggr)} \end{aligned}

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–Makeham’s Law–
The Makeham’s Law states that the force of mortality is the Gompertz failure rate plus an age-indpendent component that accounts for external causes of mortality. The following is the hazard rate function:

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\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\lambda(t)=\alpha e^{\beta t}+\mu \end{aligned} where \alpha>0, \beta>0 and \mu>0.

\text{ }

The following are the cumulative hazard rate function as well as the survival function, distribution function and the pdf of the lifetime distribution T.

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\displaystyle \begin{aligned}. \ \ \ \ \ \ \ \ \ &\Lambda(t)=\int_0^t (\alpha e^{\beta y}+\mu) dy=\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta}+\mu t \\&\text{ } \\&S_T(t)=\displaystyle e^{\displaystyle -\biggl(\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta}+\mu t\biggr)} \\&\text{ } \\&F_T(t)=\displaystyle 1-e^{\displaystyle -\biggl(\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta}+\mu t\biggr)} \\&\text{ } \\&f_T(t)=\biggl( \alpha e^{\beta t}+\mu t \biggr) \ e^{\displaystyle -\biggl(\frac{\alpha}{\beta} e^{\beta t}-\frac{\alpha}{\beta}+\mu t\biggr)} \end{aligned}

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The Tail Weight of a Distribution
The hazard rate function can provide information about the tail of a distribution. If the hazard rate function is decreasing, it is an indication that the distribution has a heavy tail, i.e., the distribution significantly puts more probability on larger values. Conversely, if the hazard rate function is increasing, it is an indication of a lighter tail. In an insurance context, heavy tailed distributions (e.g. the Pareto distribution) are suitable candidates for modeling large insurance losses (see this previous post or [1]).

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Reference

  1. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004

The mean excess loss function

We take a closer look at the mean excess loss function. We start with the following example:

Example
Suppose that an entity is exposed to a random loss X. An insurance policy offers protection against this loss. Under this policy, payment is made to the insured entity subject to a deductible d>0, i.e. when a loss is less than d, no payment is made to the insured entity, and when the loss exceeds d, the insured entity is reimbursed for the amount of the loss in excess of the deductible d. Consider the following two questions:

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  1. Of all the losses that are eligible to be reimbursed by the insurer, what is the average payment made by the insurer to the insured?
  2. What is the average payment made by the insurer to the insured entity?

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The two questions look similar. The difference between the two questions is subtle but important. In the first question, the average is computed over all losses that are eligible for reimbursement (i.e., the loss exceeds the deductible). This is the average amount the insurer is expected to pay in the event that a payment in excess of the deductible is required to be made. So this average is a per payment average.

In the second question, the average is calculated over all losses (regardless of sizes). When the loss does not reach the deductible, the payment is considered zero and when the loss is in excess of the deductible, the payment is X-d. Thus the average is the average amount the insurer has to pay per loss. So the second question is about a per loss average.

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The Mean Excess Loss Function
The average in the first question is called the mean excess loss function. Suppose X is the random loss and d>0. The mean excess loss variable is the conditional variable X-d \ \lvert X>d and the mean excess loss function e_X(d) is defined by:

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\displaystyle (1) \ \ \ \ \ e_X(d)=E(X-d \lvert X>d)

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In an insurance context, the mean excess loss function is the average payment in excess of a threshold given that the loss exceeds the threshold. In a mortality context, the mean excess loss function is called the mean residual life function and complete expectation of life and can be interpreted as the remaining time until death given that the life in question is alive at age d.

The mean excess loss function is computed by the following depending on whether the loss variable is continuous or discrete.

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\displaystyle (2) \ \ \ \ \ e_X(d)=\frac{\int_d^\infty (x-d) \ f_X(x) \ dx}{S_X(d)}

\displaystyle (3) \ \ \ \ \ e_X(d)=\frac{\sum \limits_{x>d} (x-d) \ P(X=x)}{S_X(d)}

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The mean excess loss function e_X(d) is defined only when the integral or the sum converges. The following is an equivalent calculation of e_X(d) that may be easier to use in some circumstances.

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\displaystyle (4) \ \ \ \ \ e_X(d)=\frac{\int_d^\infty S_X(x) \ dx}{S_X(d)}

\displaystyle (5a) \ \ \ \ \ e_X(d)=\frac{\sum \limits_{x \ge d} S_X(x) }{S_X(d)}

\displaystyle (5b) \ \ \ \ \ e_X(d)=\frac{\biggl(\sum \limits_{x>d} S_X(x)\biggr)+(w+1-d) S_X(w) }{S_X(d)}

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In both (5a) and (5b), we assume that the support of X is the set of nonnegative integers. In (5a), we assume that the deductible d is a positive integer. In (5b), the deductible d is free to be any positive number and w is the largest integer such that w \le d. The formulation (4) is obtained by using integration by parts (also see theorem 3.1 in [1]). The formulations of (5a) and (5b) are a result of applying theorem 3.2 in [1].

The mean excess loss function provides information about the tail weight of a distribution, see the previous post The Pareto distribution. Also see Example 3 below.
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The Mean in Question 2
The average that we need to compute is the mean of the following random variable. Note that (a)_+ is the function that assigns the value of a whenever a>0 and otherwise assigns the value of zero.

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\displaystyle (6) \ \ \ \ \ (X-d)_+=\left\{\begin{matrix}0&\ X<d\\{X-d}&\ X \ge d \end{matrix}\right.

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The mean E((X-d)_+) is calculated over all losses. When the loss is less than the deductible d, the insurer has no obligation to make a payment to the insured and the payment is assumed to be zero in the calculation of E[(X-d)_+]. The following is how this expected value is calculated depending on whether the loss X is continuous or discrete.

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\displaystyle (7) \ \ \ \ \ E((X-d)_+)=\int_d^\infty (x-d) \ f_X(x) \ dx

\displaystyle (8) \ \ \ \ \ E((X-d)_+)=\sum \limits_{x>d} (x-d) \ P(X=x)

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Based on the definitions, the following is how the two averages are related.

\displaystyle E[(X-d)_+]=e_X(d) \ [1-F_X(d)] \ \ \ \text{or} \ \ \ E[(X-d)_+]=e_X(d) \ S_X(d)

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The Limited Expected Value
For a given positive constant u, the limited loss variable is defined by

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\displaystyle (9) \ \ \ \ \ X \wedge u=\left\{\begin{matrix}X&\ X<u\\{u}&\ X \ge u \end{matrix}\right.

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The expected value E(X \wedge u) is called the limited expected value. In an insurance application, the u is a policy limit that sets a maximum on the benefit to be paid. The following is how the limited expected value is calculated depending on whether the loss X is continuous or discrete.

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\displaystyle (9) \ \ \ \ \ E(X \wedge u)=\int_{-\infty}^u x \ f_X(x) \ dx+u \ S_X(u)

\displaystyle (10) \ \ \ \ \ E(X \wedge u)=\biggl(\sum \limits_{x < u} x \ P(X=x)\biggr)+u \ S_X(u)

\text{ }

Interestingly, we have the following relation.

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\displaystyle (X-d)_+ + (X \wedge d)=X \ \ \ \ \ \ \text{and} \ \ \ \ \ \ E[(X-d)_+] + E(X \wedge d)=E(X)

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The above statement indicates that purchasing a policy with a deductible d and another policy with a policy maximum d is equivalent to buying full coverage.

Another way to interpret X \wedge d is that it is the amount of loss that is eliminated by having a deductible in the insurance policy. If the insurance policy pays the loss in full, then the insurance payment is X and the expected amount the insurer is expected to pay is E(X). By having a deductible provision in the policy, the insurer is now only liable for the amount (X-d)_+ and the amount the insurer is expected to pay per loss is E[(X-d)_+]. Consequently E(X \wedge d) is the expected amount of the loss that is eliminated by the deductible provision in the policy. The following summarizes this observation.

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\displaystyle (X \wedge d)=X-(X-d)_+ \ \ \ \ \ \ \text{and} \ \ \ \ \ \ E(X \wedge d)=E(X)-E[(X-d)_+]

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Example 1
Let the loss random variable X be exponential with pdf f(x)=\alpha e^{-\alpha x}. We have E(X)=\frac{1}{\alpha}. Because of the no memory property of the exponential distribution, given that a loss exceeds the deductible, the mean payment is the same as the original mean. Thus e_X(d)=\frac{1}{\alpha}. Then the per loss average is:

\displaystyle E[(X-d)_+]=e_X(d) \ S(d) = \frac{e^{-\alpha d}}{\alpha}

\text{ }

Thus, with a deductible provision in the policy, the insurer is expected to pay \displaystyle \frac{e^{-\alpha d}}{\alpha} per loss instead of \displaystyle \frac{1}{\alpha}. Thus the expected amount of loss eliminated (from the insurer’s point of view) is \displaystyle E(X \wedge d)=\frac{1-e^{-\alpha d}}{\alpha}.

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Example 2
Suppose that the loss variable has a Gamma distribution where the scale parameter is \alpha and the shape parameter is n=2. The pdf is \displaystyle g(x)=\alpha^2 \ x \ e^{-\alpha x}. The insurer’s expected payment without the deductible is E(X)=\frac{2}{\alpha}. The survival function S(x) is:

\displaystyle S(x)=e^{-\alpha x}(1+\alpha x)

\text{ }

For the losses that exceed the deductible, the insurer’s expected payment is:

\displaystyle \begin{aligned}e_X(d)&=\frac{\int_d^{\infty} S(x) \ dx}{S(d)}\\&=\frac{\int_d^{\infty} e^{-\alpha x}(1+\alpha x) \ dx}{e^{-\alpha d}(1+\alpha d)} \\&=\frac{\frac{e^{-\alpha d}}{\alpha}+d e^{-\alpha d}+\frac{e^{-\alpha d}}{\alpha}}{e^{-\alpha d}(1+\alpha d)} \\&=\frac{\frac{2}{\alpha}+d}{1+\alpha d} \end{aligned}

\text{ }

Then the insurer’s expected payment per loss is E[(X-d)_+]:

\displaystyle \begin{aligned}E[(X-d)_+]&=e_X(d) \ S(d) \\&=\frac{\frac{2}{\alpha}+d}{1+\alpha d} \ \ e^{-\alpha d}(1+\alpha d) \\&=e^{-\alpha d} \ \biggl(\frac{2}{\alpha}+d\biggr) \end{aligned}

\text{ }

With a deductible in the policy, the following is the expected amount of loss eliminated (from the insurer’s point of view).

\displaystyle \begin{aligned}E[X \wedge d]&=E(X)-E[(X-d)_+] \\&=\frac{2}{\alpha}-e^{-\alpha d} \ \biggl(\frac{2}{\alpha}+d\biggr) \\&=\frac{2}{\alpha}\biggl(1-e^{-\alpha d}\biggr)-d e^{-\alpha d} \end{aligned}

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Example 3
Suppose the loss variable X has a Pareto distribution with the following pdf:

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\displaystyle f_X(x)=\frac{\beta \ \alpha^\beta}{(x+\alpha)^{\beta+1}} \ \ \ \ \ x>0

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If the insurance policy is to pay the full loss, then the insurer’s expected payment per loss is \displaystyle E(X)=\frac{\alpha}{\beta-1} provided that the shape parameter \beta is larger than one.

The mean excess loss function of the Pareto distribution has a linear form that is increasing (see the previous post The Pareto distribution). The following is the mean excess loss function:

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\displaystyle e_X(d)=\frac{1}{\beta-1} \ d +\frac{\alpha}{\beta-1}=\frac{1}{\beta-1} \ d +E(X)

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If the loss is modeled by such a distribution, this is an uninsurable risk! First of all, the higher the deductible, the larger the expected payment if such a large loss occurs. The expected payment for large losses is always the unmodified expected E(X) plus a component that is increasing in d.

The increasing mean excess loss function is an indication that the Pareto distribution is a heavy tailed distribution. In general, an increasing mean excess loss function is an indication of a heavy tailed distribution. On the other hand, a decreasing mean excess loss function indicates a light tailed distribution. The exponential distribution has a constant mean excess loss function and is considered a medium tailed distribution.

Reference

  1. Bowers N. L., Gerber H. U., Hickman J. C., Jones D. A., Nesbit C. J. Actuarial Mathematics, First Edition., The Society of Actuaries, Itasca, Illinois, 1986
  2. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004

The Pareto distribution

This post takes a closer look at the Pareto distribution. A previous post demonstrates that the Pareto distribution is a mixture of exponential distributions with Gamma mixing weights. We now elaborate more on this point. Through looking at various properties of the Pareto distribution, we also demonstrate that the Pareto distribution is a heavy tailed distribution. In insurance applications, heavy-tailed distributions are essential tools for modeling extreme loss, especially for the more risky types of insurance such as medical malpractice insurance. In financial applications, the study of heavy-tailed distributions provides information about the potential for financial fiasco or financial ruin. The Pareto distribution is a great way to open up a discussion on heavy-tailed distribution.

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Update (11/12/2017). This blog post introduces a catalog of many other parametric severity models in addition to Pareto distribution. The link to the catalog is found in that blog post. To go there directly, this is the link.

Update (10/29/2017). This blog post has updated information on Pareto distribution. It also has links to more detailed contents on Pareto distribution in two companion blogs. These links are also given here: more detailed post on Pareto, Pareto Type I and Type II and practice problems on Pareto.

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The continuous random variable X with positive support is said to have the Pareto distribution if its probability density function is given by

\displaystyle f_X(x)=\frac{\beta \ \alpha^\beta}{(x+\alpha)^{\beta+1}} \ \ \ \ \ x>0

where \alpha>0 and \beta>0 are constant. The constant \alpha is the scale parameter and \beta is the shape parameter. The following lists several other distributional quantities of the Pareto distribution, which will be used in the discussion below.

\displaystyle S_X(x)=\frac{\alpha^\beta}{(x+\alpha)^\beta}=\biggl(\frac{\alpha}{x+\alpha}\biggr)^\beta \ \ \ \ \ \ \ \ \ \text{survival function}

\displaystyle F_X(x)=1-\biggl(\frac{\alpha}{x+\alpha}\biggr)^\beta \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{distribution function}

\displaystyle E(X)=\frac{\alpha}{\beta-1} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{mean},\beta>1

\displaystyle E(X^2)=\frac{2 \alpha^2}{(\beta-1)(\beta-2)} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{second momemt},\beta>2

\displaystyle Var(X)=\frac{\alpha^2 \beta}{(\beta-1)^2(\beta-2)} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{variance},\beta>2

\displaystyle E(X^k)=\frac{k! \alpha^k}{(\beta-1) \cdots (\beta-k)} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \text{higher moments},\beta>k, \text{ k positive integer}

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The Pareto Distribution as a Mixture
The Pareto pdf indicated above can be obtained by mixing exponential distributions using Gamma distributions as weights. Suppose that X follows an exponential distribution (conditional on a parameter value \theta). The following is the conditional pdf of X.

\displaystyle f_{X \lvert \Theta}(x \lvert \theta)=\theta e^{-\theta x} \ \ \ x>0

There is uncertainty in the parameter, which can be viewed as a random variable \Theta. Suppose that \Theta follows a Gamma distribution with scale parameter \alpha and shape parameter \beta. The following is the pdf of \Theta.

\displaystyle f_{\Theta}(\theta)=\frac{\alpha^\beta}{\Gamma(\beta)} \ \theta^{\beta-1} \ e^{-\alpha \theta} \ \ \ \theta>0

The unconditional pdf of X is the weighted average of the conditional pdfs with the Gamma pdf as weight.

\displaystyle \begin{aligned}f_X(x)&=\int_0^{\infty} f_{X \lvert \Theta}(x \lvert \theta) \ f_\Theta(\theta) \ d \theta \\&=\int_0^{\infty} \biggl[\theta \ e^{-\theta x}\biggr] \ \biggl[\frac{\alpha^\beta}{\Gamma(\beta)} \ \theta^{\beta-1} \ e^{-\alpha \theta}\biggr] \ d \theta \\&=\int_0^{\infty} \frac{\alpha^\beta}{\Gamma(\beta)} \ \theta^\beta \ e^{-\theta(x+\alpha)} \ d \theta \\&=\frac{\alpha^\beta}{\Gamma(\beta)} \frac{\Gamma(\beta+1)}{(x+\alpha)^{\beta+1}} \int_0^{\infty} \frac{(x+\alpha)^{\beta+1}}{\Gamma(\beta+1)} \ \theta^{\beta+1-1} \ e^{-\theta(x+\alpha)} \ d \theta \\&=\frac{\beta \ \alpha^\beta}{(x+\alpha)^{\beta+1}} \end{aligned}

In the following discussion, X will denote the Pareto distribution as defined above. As will be shown below, the exponential distribution is considered a light tailed distribution. Yet mixing exponentials produces the heavy tailed Pareto distribution. Mixture distributions tend to heavy tailed (see [1]). The Pareto distribution is a handy example.

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The Tail Weight of the Pareto Distribution
When a distribution significantly puts more probability on larger values, the distribution is said to be a heavy tailed distribution (or said to have a larger tail weight). According to [1], there are four ways to look for indication that a distribution is heavy tailed.

  1. Existence of moments.
  2. Speed of decay of the survival function to zero.
  3. Hazard rate function.
  4. Mean excess loss function.

Existence of moments
Note that the existence of the Pareto higher moments E(X^k) is capped by the shape parameter \beta. In particular, the mean E(X)=\frac{\alpha}{\beta-1} does not exist for \beta \le 1. If the Pareto distribution is to model a random loss, and if the mean is infinite (when \beta=1), the risk is uninsurable! On the other hand, when \beta=2, the Pareto variance does not exist. This shows that for a heavy tailed distribution, the variance may not be a good measure of risk.

For a given random variable Z, the existence of all moments E(Z^k), for all positive integers k, indicates with a light (right) tail for the distribution of Z. The existence of positive moments exists only up to a certain value of a positive integer k is an indication that the distribution has a heavy right tail. In contrast, the exponential distribution and the Gamma distribution are considered to have light tails since all moments exist.

The speed of decay of the survival function
The survival function S_X(x)=P(X>x) captures the probability of the tail of a distribution. If a distribution whose survival function decays slowly to zero (equivalently the cdf goes slowly to one), it is another indication that the distribution is heavy tailed.

The following is a comparison of a Pareto survival function and an exponential survival function. The Pareto survival function has parameters (\alpha=2 and \beta=2). The two survival functions are set to have the same 75th percentile (x=2).

\displaystyle \begin{pmatrix} \text{x}&\text{Pareto }S_X(x)&\text{Exponential }S_Y(x)&\displaystyle \frac{S_X(x)}{S_Y(x)} \\\text{ }&\text{ }&\text{ }&\text{ } \\{2}&0.25&0.25&1 \\{10}&0.027777778&0.000976563&28  \\{20}&0.008264463&9.54 \times 10^{-7}&8666 \\{30}&0.00390625&9.31 \times 10^{-10}&4194304 \\{40}&0.002267574&9.09 \times 10^{-13}&2.49 \times 10^{9} \\{60}&0.001040583&8.67 \times 10^{-19}&1.20 \times 10^{15} \\{80}&0.000594884&8.27 \times 10^{-25}&7.19 \times 10^{20} \\{100}&0.000384468&7.89 \times 10^{-31}&4.87 \times 10^{26} \\{120}&0.000268745&7.52 \times 10^{-37}&3.57 \times 10^{32} \\{140}&0.000198373&7.17 \times 10^{-43}&2.76 \times 10^{38} \\{160}&0.000152416&6.84 \times 10^{-49}&2.23 \times 10^{44} \\{180}&0.000120758&6.53 \times 10^{-55}&1.85 \times 10^{50}  \end{pmatrix}

Note that at the large values, the Pareto right tails retain much more probability. This is also confirmed by the ratio of the two survival functions, with the ratio approaching infinity. If a random loss is a heavy tailed phenomenon that is described by the above Pareto survival function (\alpha=2 and \beta=2), then the above exponential survival function is woefully inadequate as a model for this phenomenon even though it may be a good model for describing the loss up to the 75th percentile. It is the large right tail that is problematic (and catastrophic)!

Since the Pareto survival function and the exponential survival function have closed forms, We can also look at their ratio.

\displaystyle \frac{\text{pareto survival}}{\text{exponential survival}}=\frac{\displaystyle \frac{\alpha^\beta}{(x+\alpha)^\beta}}{e^{-\lambda x}}=\frac{\alpha^\beta e^{\lambda x}}{(x+\alpha)^\beta} \longrightarrow \infty \ \text{ as } x \longrightarrow \infty

In the above ratio, the numerator has an exponential function with a positive quantity in the exponent, while the denominator has a polynomial in x. This ratio goes to infinity as x \rightarrow \infty.

In general, whenever the ratio of two survival functions diverges to infinity, it is an indication that the distribution in the numerator of the ratio has a heavier tail. When the ratio goes to infinity, the survival function in the numerator is said to decay slowly to zero as compared to the denominator. We have the same conclusion in comparing the Pareto distribution and the Gamma distribution, that the Pareto is heavier in the tails. In comparing the tail weight, it is equivalent to consider the ratio of density functions (due to the L’Hopital’s rule).

\displaystyle \lim_{x \rightarrow \infty} \frac{S_1(x)}{S_2(x)}=\lim_{x \rightarrow \infty} \frac{S_1^{'}(x)}{S_2^{'}(x)}=\lim_{x \rightarrow \infty} \frac{f_1(x)}{f_2(x)}

The Hazard Rate Function
The hazard rate function h_X(x) of a random variable X is defined as the ratio of the density function and the survival function.

\displaystyle h_X(x)=\frac{f_X(x)}{S_X(s)}

The hazard rate is called the force of mortality in a life contingency context and can be interpreted as the rate that a person aged x will die in the next instant. The hazard rate is called the failure rate in reliability theory and can be interpreted as the rate that a machine will fail at the next instant given that it has been functioning for x units of time. The following is the hazard rate function of the Pareto distribution.

\displaystyle \begin{aligned}h_X(x)&=\frac{f_X(s)}{S_X(x)} \\&=\frac{\beta}{x+\alpha}  \end{aligned}

The interesting point is that the Pareto hazard rate function is an decreasing function in x. Another indication of heavy tail weight is that the distribution has a decreasing hazard rate function. One key characteristic of hazard rate function is that it can generate the survival function.

\displaystyle S_X(x)=e^{\displaystyle -\int_0^x h_X(t) \ dt}

Thus if the hazard rate function is decreasing in x, then the survival function will decay more slowly to zero. To see this, let H_X(x)=\int_0^x h_X(t) \ dt, which is called the cumulative hazard rate function. As indicated above, the survival function can be generated by e^{-H_X(x)}. If h_X(x) is decreasing in x, H_X(x) is smaller than H_Y(x) where h_Y(x) is constant in x or increasing in x. Consequently e^{-H_X(x)} is decaying to zero much more slowly than e^{-H_Y(x)}.

In contrast, the exponential distribution has a constant hazard rate function, making it a medium tailed distribution. As explained above, any distribution having an increasing hazard rate function is a light tailed distribution.

The Mean Excess Loss Function
Suppose that a property owner is exposed to a random loss Y. The property owner buys an insurance policy with a deductible d such that the insurer will pay a claim in the amount of Y-d if a loss occurs with Y>d. The insuerer will pay nothing if the loss is below the deductible. Whenever a loss is above d, what is the average claim the insurer will have to pay? This is one way to look at mean excess loss function, which represents the expected excess loss over a threshold conditional on the event that the threshold has been exceeded.

Given a loss variable Y and given a deductible d>0, the mean excess loss function is e_Y(d)=E(Y-d \lvert X>d). For a continuous random variable, it is computed by

\displaystyle e_Y(d)=\frac{\int_d^{\infty} (y-d) \ f_Y(y) \ dy}{S_Y(d)}

Applying the technique of integration by parts produces the following formula:

\displaystyle e_Y(d)=\frac{\int_d^{\infty} S_Y(y) \ dy}{S_Y(d)}

It turns out that the mean excess loss function is one more way to examine the tail property of a distribution. The following is the mean excess loss function of the Pareto distribution:

\displaystyle e_X(d)=\frac{d+\alpha}{\beta-1}=\frac{1}{\beta-1} \ d + \frac{\alpha}{\beta-1}

Note that the Pareto mean excess loss function is a linear increasing function of the deductible d. This means that the larger the deductible, the larger the expected claim if such a large loss occurs! If a random loss is modeled by such a distribution, it is a catastrophic risk situation. In general, an increasing mean excess loss function is an indication of a heavy tailed distribution. On the other hand, a decreasing mean excess loss function indicates a light tailed distribution. The exponential distribution has a constant mean excess loss function and is considered a medium tailed distribution.

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The Pareto distribution has many economic applications. Since it is a heavy tailed distribution, it is a good candidate for modeling income above a theoretical value and the distribution of insurance claims above a threshold value.

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Reference

  1. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004

The variance of a mixture

Suppose X is a mixture distribution that is the result of mixing a family of conditional distributions indexed by a parameter random variable \Theta. The uncertainty in the parameter variable \Theta has the effect of increasing the unconditional variance of the mixture X. Thus, Var(X) is not simply the weighted average of the conditional variance Var(X \lvert \Theta). The unconditional variance Var(X) is the sum of two components. They are:

\displaystyle Var(X)=E[Var(X \lvert \Theta)]+Var[E(X \lvert \Theta)]

The above relationship is called the law of total variance, which is the proper way of computing the unconditional variance Var(X). The first component E[Var(X \lvert \Theta)] is called the expected value of conditional variances, which is the weighted average of the conditional variances. The second component Var[E(X \lvert \Theta)] is called the variance of the conditional means, which represents the additional variance as a result of the uncertainty in the parameter \Theta.

We use an example of a two-point mixture to illustrate the law of total variance. The example is followed by a proof of the total law of variance.

Example
Let U be the uniform distribution on the unit interval (0, 1). Suppose that a large population of insureds is composed of “high risk” and “low risk” individuals. The proportion of insured classified as “low risk” is p where 0<p<1. The random loss amount X of a “low risk” insured is U. The random loss amount X of a “high risk” insured is U shifted by a positive constant w>0, i.e. w+U. What is the variance of the loss amount of an insured randomly selected from this population?

For convenience, we use \Theta as a parameter to indicate the risk class (\Theta=1 is “low risk” and \Theta=2 is “high risk”). The following shows the relevant conditional distributional quantities of X.

\displaystyle E(X \lvert \Theta=1)=\frac{1}{2} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ E(X \lvert \Theta=2)=w+\frac{1}{2}

\displaystyle Var(X \lvert \Theta=1)=\frac{1}{12} \ \ \ \ \ \ \ \ \ \ \ \ Var(X \lvert \Theta=2)=\frac{1}{12}

The unconditional mean loss is the weighted average of the conditional mean loss amounts. However, the same idea does not work for variance.

\displaystyle \begin{aligned}E[X]&=p \times E(X \lvert \Theta=1)+(1-p) \times E(X \lvert \Theta=2) \\&=p \times \frac{1}{2}+(1-p) \times (w+\frac{1}{2}) \\&=\frac{1}{2}+(1-p) \ w \end{aligned}

\displaystyle \begin{aligned}Var[X]&\ne p \times Var(X \lvert \Theta=1)+(1-p) \times Var(X \lvert \Theta=2)=\frac{1}{12}  \end{aligned}

The conditional variance is the same for both risk classes since the “high risk” loss is a shifted distribution of the “low risk” loss. However, the unconditional variance is more than \frac{1}{12} since the mean loss for the two casses are different (heterogeneous risks across the classes). The uncertainty in the risk classes (i.e. uncertainty in the parameter \Theta) introduces additional variance in the loss for a randomly selected insured. The unconditional variance Var(X) is the sum of the following two components:

\displaystyle \begin{aligned}E[Var(X \lvert \Theta)]&=p \times Var(X \lvert \Theta=1)+(1-p) \times Var(X \lvert \Theta=2) \\&=p \times \frac{1}{12}+(1-p) \times \frac{1}{12} \\&=\frac{1}{12} \end{aligned}

\displaystyle \begin{aligned}Var[E(X \lvert \Theta)]&=p \times E(X \lvert \Theta=1)^2+(1-p) \times E(X \lvert \Theta=2)^2 \\&\ \ \ -\biggl(p \times E(X \lvert \Theta=1)+(1-p) \times E(X \lvert \Theta=2)\biggr)^2 \\&=p \times \biggl(\frac{1}{2}\biggr)^2+(1-p) \times \biggl(w+\frac{1}{2}\biggr)^2 \\&\ \ \ -\biggl(p \times \frac{1}{2}+(1-p) \times (w+\frac{1}{2})\biggr)^2 \\&=p \ (1-p) \ w^2 \end{aligned}

\displaystyle \begin{aligned}Var(X)&=E[Var(X \lvert \Theta)]+Var[E(X \lvert \Theta)] \\&=\frac{1}{12}+p \ (1-p) \ w^2  \end{aligned}

The additional variance is in the amount of p(1-p)w^2. This is the variance of the conditional means of the risk classes. Note that w is the additional mean loss for a “high risk” insured. The higher the additional mean loss w, the more heterogeneous in risk between the two classes, hence the larger the dispersion in unconditional loss.

The total law of variance gives the unconditional variance of a random variable X that is indexed by another random variable \Theta. The unconditional variance of X is the sum of two components, namely, the expected value of conditional variances and the variance of the conditional means. The formula is:

\displaystyle Var(X)=E[Var(X \lvert \Theta)]+Var[E(X \lvert \Theta)]

The following is the derivation of the formula:

\displaystyle \begin{aligned}Var[X]&=E[X^2]-E[X]^2 \\&=E[E(X^2 \lvert \Theta)]-E[E(X \lvert \Theta)]^2 \\&=E\left\{Var(X \lvert \Theta)+E(X \lvert \Theta)^2 \right\}-E[E(X \lvert \Theta)]^2 \\&=E[Var(X \lvert \Theta)]+E[E(X \lvert \Theta)^2]-E[E(X \lvert \Theta)]^2 \\&=E[Var(X \lvert \Theta)]+Var[E(X \lvert \Theta)] \end{aligned}

Additional Practice
See this blog post for practice problems on mixture distributions.

Reference

  1. Klugman S.A., Panjer H. H., Wilmot G. E. Loss Models, From Data to Decisions, Second Edition., Wiley-Interscience, a John Wiley & Sons, Inc., New York, 2004