Gompertz distribution

Gompertz distribution

Probability density function Note: b=2.322
Cumulative distribution function
Parameters	$\eta, b > 0\,\!$
Support	$x \in [0, \infty)\!$
PDF	$b\eta e^{bx}e^{\eta}\exp\left(-\eta e^{bx} \right)$
CDF	$1-\exp\left(-\eta\left(e^{bx}-1 \right)\right)$
Mean	$(-1/b)e^{\eta}\text{Ei}\left(-\eta\right)$ $\text {where Ei}\left(z\right)=\int\limits_{-z}^{\infin}\left(e^{-v}/v\right)dv$
Median	$\left(1/b\right)\ln\left[\left(-1/\eta\right)\ln\left(1/2\right)+1\right]$
Mode	$=\left(1/b\right)\ln \left(1/\eta\right)\$ $\text {with }0 <\text {F}\left(x^*\right)<1-e^{-1} = 0.632121, 0<\eta<1$ $=0, \quad \eta \ge 1$
Variance	$\left(1/b\right)^2 e^{\eta}\{-2\eta { \ }_3\text {F}_3 \left(1,1,1;2,2,2;-\eta\right)+\gamma^2$$+\left(\pi^2/6\right)+2\gamma\ln\left(\eta\right)+[\ln\left(\eta\right)]^2-e^{\eta}[\text{Ei}\left(-\eta \right)]^2\}$ $\begin{align}\text{ where } &\gamma \text{ is the Euler constant: }\,\!\\ &\gamma=-\psi\left(1\right)=\text{0.5777215... }\end{align}$$\begin{align}\text { and } { }_3\text {F}_3&\left(1,1,1;2,2,2;-z\right)=\\&\sum_{k=0}^\infty\left[1/\left(k+1\right)^3\right]\left(-1\right)^k\left(z^k/k!\right)\end{align}$
MGF	$\text{E}\left(e^{-t x}\right)=\eta e^{\eta}\text{E}_{t/b}\left(\eta\right)$ $\text{with E}_{t/b}\left(\eta\right)=\int_1^\infin e^{-\eta v} v^{-t/b}dv,\ t>0$

In probability and statistics, the Gompertz distribution is a continuous probability distribution. It has been used as a model of customer lifetime.

Specification

Probability density function

The probability density function of the Gompertz distribution is:

$f\left(x;\eta, b\right)=b\eta e^{bx}e^{\eta}\exp\left(-\eta e^{bx} \right)\text{for }x \geq 0, \,$

where $b > 0\,\!$ is the scale parameter and $\eta > 0\,\!$ is the shape parameter of the Gompertz distribution. In the actuarial and biological sciences and in demography, the Gompertz distribution is parametrized slightly differently (Gompertz–Makeham law of mortality).

Cumulative distribution function

The cumulative distribution function of the Gompertz distribution is:

$F\left(x;\eta, b\right)= 1-\exp\left(-\eta\left(e^{bx}-1 \right)\right)$

where $\eta, b>0, x \geq 0. \,$.

Moment generating function

The moment generating function is:

$\text{E}\left(e^{-t X}\right)=\eta e^{\eta}\text{E}_{t/b}\left(\eta\right)$

where

$\text{E}_{t/b}\left(\eta\right)=\int_1^\infin e^{-\eta v} v^{-t/b}dv,\ t>0.$

Properties

The Gompertz distribution is a flexible distribution that can be skewed to the right and to the left.

Shapes

The Gompertz density function can take on different shapes depending on the values of the shape parameter $\eta\,\!$:

• $\eta \geq 1\,$ the probability density function has its mode at 0.
• $0 < \eta < 1\,$ the probability density function has its mode at

$x^*=\left(1/b\right)\ln \left(1/\eta\right)\text {with }0 < F\left(x^*\right)<1-e^{-1} = 0.632121$

Related distributions

• If X is defined to be the result of sampling from a Gumbel distribution until a negative value Y is produced, and setting X=−Y, then X has a Gompertz distribution.
• The gamma distribution is a natural conjugate prior to a Gompertz likelihood with known scale parameter $b \,\!$.^{[citation needed]}
• When $\eta\,\!$ varies according to a gamma distribution with shape parameter $\alpha\,\!$ and scale parameter $\beta\,\!$ (mean = $\alpha\beta\,\!$), the distribution of x is Gamma/Gompertz.^{[citation needed]}

See also

• Gompertz function
• Customer lifetime value

References

• Bemmaor, Albert C.; Glady, Nicolas (2011). "Modeling Purchasing Behavior With Sudden 'Death': A Flexible Customer Lifetime Model". Management Science. http://mansci.journal.informs.org/content/early/2011/12/02/mnsc.1110.1461.abstract. 
• Bemmaor, Albert C.; Glady, Nicolas (2011). "Implementing the Gamma/Gompertz/NBD Model in MATLAB". Cergy-Pontoise: ESSEC Business School. http://dl.dropbox.com/u/7097708/gg_nbd_MATLAB.pdf. 
• Gompertz, B. (1825). "On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies". Philosophical Transactions of the Royal Society of London 115: 513–583. 
• Johnson, Norman L.; Kotz, Samuel; Balakrishnan, N. (1995). Continuous Univariate Distributions. 2 (2nd ed.). New York: John Wiley & Sons. ISBN 0-471-58494-0. 
• Sheikh, A. K.; Boah, J. K.; Younas, M. (1989). "Truncated Extreme Value Model for Pipeline Reliability". Reliability Engineering and System Safety 25 (1): 1–14.