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A generalized chi-square variable or distribution can be parameterized in two ways. The first is in terms of the weights w i {\displaystyle w_{i}} , the degrees of freedom k i {\displaystyle k_{i}} and non-centralities λ i {\displaystyle \lambda _{i}} of the constituent non-central chi-squares, and the coefficients s {\displaystyle s} and m ...
The generalized additive model for location, scale and shape (GAMLSS) is a semiparametric regression model in which a parametric statistical distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables.
In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unmeasured correlation between observations from different timepoints. [1] [2]
Type IV probability density functions (means=0, variances=1) The Type IV generalized logistic, or logistic-beta distribution, with support and shape parameters , >, has (as shown above) the probability density function (pdf):
The two generalized normal families described here, like the skew normal family, are parametric families that extends the normal distribution by adding a shape parameter. Due to the central role of the normal distribution in probability and statistics, many distributions can be characterized in terms of their relationship to the normal ...
In statistics, the generalized Dirichlet distribution (GD) is a generalization of the Dirichlet distribution with a more general covariance structure and almost twice the number of parameters. Random vectors with a GD distribution are completely neutral. [1] The density function of , …, is
In econometrics and statistics, the generalized method of moments (GMM) is a generic method for estimating parameters in statistical models.Usually it is applied in the context of semiparametric models, where the parameter of interest is finite-dimensional, whereas the full shape of the data's distribution function may not be known, and therefore maximum likelihood estimation is not applicable.
The following shows how to implement a location–scale family in a statistical package or programming environment where only functions for the "standard" version of a distribution are available. It is designed for R but should generalize to any language and library.