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Using the fact that a Gamma(1, 1) distribution is the same as an Exp(1) distribution, and noting the method of generating exponential variables, we conclude that if U is uniformly distributed on (0, 1], then −ln U is distributed Gamma(1, 1) (i.e. inverse transform sampling).
Since many distributions commonly used for parametric models in survival analysis (such as the exponential distribution, the Weibull distribution and the gamma distribution) are special cases of the generalized gamma, it is sometimes used to determine which parametric model is appropriate for a given set of data. [1]
A key result in Efron's seminal paper that introduced the bootstrap [4] is the favorable performance of bootstrap methods using sampling with replacement compared to prior methods like the jackknife that sample without replacement. However, since its introduction, numerous variants on the bootstrap have been proposed, including methods that ...
In statistics, the Wishart distribution is a generalization of the gamma distribution to multiple dimensions. It is named in honor of John Wishart , who first formulated the distribution in 1928. [ 1 ]
In statistics, a matrix gamma distribution is a generalization of the gamma distribution to positive-definite matrices. [1] It is effectively a different parametrization of the Wishart distribution, and is used similarly, e.g. as the conjugate prior of the precision matrix of a multivariate normal distribution and matrix normal distribution.
The chi-squared distribution is a special case of the gamma distribution, in that (,) using the rate parameterization of the gamma distribution (or (,) using the scale parameterization of the gamma distribution) where k is an integer.
Also known as the (Moran-)Gamma Process, [1] the gamma process is a random process studied in mathematics, statistics, probability theory, and stochastics. The gamma process is a stochastic or random process consisting of independently distributed gamma distributions where N ( t ) {\displaystyle N(t)} represents the number of event occurrences ...
The Nakagami distribution or the Nakagami-m distribution is a probability distribution related to the gamma distribution. The family of Nakagami distributions has two parameters: a shape parameter m ≥ 1 / 2 {\displaystyle m\geq 1/2} and a scale parameter Ω > 0 {\displaystyle \Omega >0} .