Gompertz distribution
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Probability density function
Gompertz distributionNote: b=2.322 |
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Cumulative distribution function
Gompertz cumulative distribution |
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Parameters | |
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Support | |
CDF | |
Mean | |
Median | |
Mode | |
Variance | |
MGF |
In probability and statistics, the Gompertz distribution is a continuous probability distribution, named after Benjamin Gompertz (1779 - 1865). The Gompertz distribution is often applied to describe the distribution of adult lifespans by demographers[1][2] and actuaries.[3][4] Related fields of science such as biology[5] and gerontology[6] also considered the Gompertz distribution for the analysis of survival. More recently, computer scientists have also started to model the failure rates of computer codes by the Gompertz distribution.[7] In Marketing Science, it has been used as an individual-level simulation for customer lifetime value modeling.[8] In network theory, particularly the Erdős–Rényi model, the walk length of a random self-avoiding walk (SAW) is distributed according to the Gompertz distribution.[9]
Contents
Specification
Probability density function
The probability density function of the Gompertz distribution is:
where is the scale parameter and 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:
where and
Moment generating function
The moment generating function is:
where
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 :
- When the probability density function has its mode at 0.
- When the probability density function has its mode at
Kullback-Leibler divergence
If and are the probability density functions of two Gompertz distributions, then their Kullback-Leibler divergence is given by
where denotes the exponential integral and is the upper incomplete gamma function.[10]
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 [8]
- When varies according to a gamma distribution with shape parameter and scale parameter (mean = ), the distribution of is Gamma/Gompertz.[8]
See also
- Gompertz-Makeham law of mortality
- Gompertz function
- Customer lifetime value
- Gamma Gompertz distribution
Notes
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- ↑ 8.0 8.1 8.2 Lua error in package.lua at line 80: module 'strict' not found.
- ↑ Tishby, Biham, Katzav (2016), The distribution of path lengths of self avoiding walks on Erdős-Rényi networks, arXiv:1603.06613.
- ↑ Bauckhage, C. (2014), Characterizations and Kullback-Leibler Divergence of Gompertz Distributions, arXiv:1402.3193.
References
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