Volume 15, Issue 1, March 2016, Pages 25 - 35
Parameter Estimation in Gamma Mixture Model using Normal-based Approximation
Authors
R. Vani Lakshmi, V.S. Vaidyanathan
Corresponding Author
R. Vani Lakshmi
Received 28 March 2015, Accepted 6 October 2015, Available Online 1 March 2016.
- DOI
- 10.2991/jsta.2016.15.1.3How to use a DOI?
- Keywords
- Gamma Mixture Model; gammamixEM(); Maximum Likelihood; MCLUST; Mean Square Error; Wilson-Hilferty Approximation.
- Abstract
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.
- Copyright
- © 2017, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - R. Vani Lakshmi AU - V.S. Vaidyanathan PY - 2016 DA - 2016/03/01 TI - Parameter Estimation in Gamma Mixture Model using Normal-based Approximation JO - Journal of Statistical Theory and Applications SP - 25 EP - 35 VL - 15 IS - 1 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.2016.15.1.3 DO - 10.2991/jsta.2016.15.1.3 ID - VaniLakshmi2016 ER -