Parameter Estimation in Gamma Mixture Model using Normal-based Approximation
- Resource Type
- article
- Authors
- R. Vani Lakshmi; V.S. Vaidyanathan
- Source
- Journal of Statistical Theory and Applications (JSTA), Vol 15, Iss 1 (2016)
- Subject
- Gamma Mixture Model
gammamixEM()
Maximum Likelihood
MCLUST
Mean Square Error
Wilson-Hilferty Approximation.
Probabilities. Mathematical statistics
QA273-280
- Language
- English
- ISSN
- 1538-7887
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.