a vector of positive numbers. NOTE: <code>data</code> has to be the rescaled data, i.e. devided by the estimated <code>scale</code> parameter!

data

value of the estimated <code>shape</code> parameter.

k_estimator

This function computes the goodness-of-fit test statistic for the gamma family in the spirit of Cramer and von Mises. Note that this tests the composite hypothesis of fit to the family of gamma distributions, i.e. a bootstrap procedure is implemented to perform the test, see <code><a rd-options="" href="/link/crit.values?package=gofgamma&version=1.0" data-mini-rdoc="gofgamma::crit.values">crit.values</a></code>.

We implement various classical tests for the composite hypothesis of testing the fit to the family of gamma distributions as the Kolmogorov-Smirnov test, the Cramer-von Mises test, the Anderson Darling test and the Watson test. For each test a parametric bootstrap procedure is implemented, as considered in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851>. The recent procedures presented in Henze, Meintanis & Ebner (2012) <doi:10.1080/03610926.2010.542851> and Betsch & Ebner (2019) <doi:10.1007/s00184-019-00708-7> are implemented. Estimation of parameters of the gamma law are implemented using the method of Bhattacharya (2001) <doi:10.1080/00949650108812100>.

Bruno Ebner

gofgamma

Goodness-of-Fit Tests for the Gamma Distribution

Lucas Butsch

Steffen Betsch

CM function

This function computes the goodness-of-fit test statistic for the gamma family in the spirit of Cramer and von Mises. Note that this tests the composite hypothesis of fit to the family of gamma distributions, i.e. a bootstrap procedure is implemented to perform the test, see <code><a rd-options='' href='crit.values'>crit.values</a></code>.

CM: statistic of the Cramer-von Mises goodness-of-fit test for the gamma family

Description

Usage

Arguments

Value

Details

References

Examples