Parameter Estimation in Gamma Mixture Model using Normal-based Approximation
Vani Lakshmi R, V. S. Vaidyanathan · Journal of Statistical Theory and Applications · 2016
Gamma mixture models have wide applications in hydrology, finance and reliability.Parameter estimation in this class of models is a challenging task owing to the complexity associated with the model structure.In this paper, a novel approach is proposed to estimate the parameters of Gamma mixture models using Wilson-Hilferty normalbased approximation method.The proposed methodology uses a popular clustering algorithm for Gaussian mixtures namely, MCLUST and a confidence interval based search approach to obtain the estimates.The methodology is implemented on simulated as well as real-life datasets and its performance is compared with gammamixEM() function available in R.