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 9 articles )
Showing results 1 to 9 of 9.
- Villa, Cristiano: Bayesian estimation of the threshold of a generalised Pareto distribution for heavy-tailed observations (2017)
- 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)
- Rubio, F.J.; Steel, M.F.J.: Bayesian modelling of skewness and kurtosis with two-piece scale and shape distributions (2015)
- Venturini, Sergio; Dominici, Francesca; Parmigiani, Giovanni: Generalized quantile treatment effect: a flexible Bayesian approach using quantile ratio smoothing (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)
- Carvalho, Alexandre X.; Skoulakis, Georgios: Time series mixtures of generalized $t$ experts: ML estimation and an application to stock return density forecasting (2010)
- Venturini, Sergio; Dominici, Francesca; Parmigiani, Giovanni: Gamma shape mixtures for heavy-tailed distributions (2008)