ExponentialFamily

ExponentialFamily
is an option for GeneralizedLinearModelFit that specifies the exponential family for the model.

DetailsDetails

  • ExponentialFamily specifies the assumed distribution for the independent observations modeled by .
  • The density function for an exponential family can be written in the form for functions , , , , and , random variable , canonical parameter , and dispersion parameter .
  • Possible parametric distributions include: , , , , .
  • The observed responses are restricted to the domains of parametric distributions as follows:
  • "Binomial"
    "Gamma"
    "Gaussian"
    "InverseGaussian"
    "Poisson"
  • The setting ExponentialFamily->"QuasiLikelihood", defines a quasi-likelihood function, used for a maximum likelihood fit.
  • The log quasi-likelihood function for the response and prediction is given by , where is the dispersion parameter and is the variance function. The dispersion parameter is estimated from input data and can be controlled through the option DispersionEstimatorFunction.
  • The setting ExponentialFamily->{"QuasiLikelihood",opts} allows the following quasi-likelihood suboptions to be specified:
  • "ResponseDomain"Function[y,y>0]domain for responses
    "VarianceFunction"Function[μ,1]variance as function of mean
  • The parametric distributions can be emulated with quasi-likelihood structures by using the following and suboption settings:
  • 0<=y<=1
  • variants of and families can be used to model overdispersed () or underdispersed () data, different from the theoretical dispersion ().
  • Common variance functions, response domains, and uses include:
  • power models, actuarial science, meteorology, etc.
    probability models, binomial related, etc.
    counting models, Poisson related, etc.

ExamplesExamplesopen allclose all

Basic Examples  (1)Basic Examples  (1)

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Fit data to a simple linear regression model:

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Fit to a canonical gamma regression model:

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Fit to a canonical inverse Gaussian regression model:

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Introduced in 2008
(7.0)