GSM: Gamma Shape Mixture: This package implements a Bayesian approach for estimation of a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.
Keywords for this software
References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Chen, JiaHua; Li, ShaoTing; Tan, XianMing: Consistency of the penalized MLE for two-parameter gamma mixture models (2016)
- Rebafka, T.; Roueff, F.: Nonparametric estimation of the mixing density using polynomials (2015)
- Cui, Kai: Semiparametric Gaussian variance-mean mixtures for heavy-tailed and skewed data (2012)
- Trippa, Lorenzo; Parmigiani, Giovanni: False discovery rates in somatic mutation studies of cancer (2011)
- Venturini, Sergio; Dominici, Francesca; Parmigiani, Giovanni: Gamma shape mixtures for heavy-tailed distributions (2008)