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Gamma process
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A gamma process, also called the Moran-Gamma subordinator,[1] is a two-parameter stochastic process which models the accumulation of effort or wear over time. The gamma process has independent and stationary increments which follow the gamma distribution, hence the name. The gamma process is studied in mathematics, statistics, probability theory, and stochastics, with particular applications in deterioration modeling[2] and mathematical finance.[3]
![]() | This article may be too technical for most readers to understand. (October 2021) |

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Notation
The gamma process is often abbreviated as where represents the time from 0. The shape parameter (inversely) controls the jump size, and the rate parameter controls the rate of jump arrivals, analogously with the gamma distribution.[4] Both and must be greater than 0. We use the gamma function and gamma distribution in this article, so the reader should distinguish between (the gamma function), (the gamma distribution), and (the gamma process).
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Definition
The process is a pure-jump increasing Lévy process with intensity measure for all positive . It is assumed that the process starts from a value 0 at meaning . Thus jumps whose size lies in the interval occur as a Poisson process with intensity
The process can also be defined as a stochastic process with and independent increments, whose marginal distribution of the random variable for an increment is given by[4]
Inhomogenous process
It is also possible to allow the shape parameter to vary as a function of time, .[4]
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Properties
This section needs additional citations for verification. (June 2023) |
Mean and variance
Because the value at each time has mean and variance [5] the gamma process is sometimes also parameterised in terms of the mean () and variance () of the increase per unit time. These satisfy and .
Scaling
Multiplication of a gamma process by a scalar constant is again a gamma process with different mean increase rate.
Adding independent processes
The sum of two independent gamma processes is again a gamma process.
Moments
The moment function helps mathematicians find expected values, variances, skewness, and kurtosis. where is the Gamma function.
Moment generating function
The moment generating function is the expected value of where X is the random variable.
Correlation
Correlation displays the statistical relationship between any two gamma processes. , for any gamma process
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Related processes
The gamma process is used as the distribution for random time change in the variance gamma process. Specifically, combining Brownian motion with a gamma process produces a variance gamma process,[6] and a variance gamma process can be written as the difference of two gamma processes.[3]
See also
Notes
References
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