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Estimation of parameters in a finite mixture of multivariate gamma distributions using gaussian approximation

Authors:

V. S. Vaidyanathan ,

Pondicherry University, Puducherry- 605 014, IN
About V. S.
Department of Statistics
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R. Vani Lakshmi

Pondicherry University, Puducherry- 605 014, IN
About R. Vani
Department of Statistics
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Abstract

Finite mixture of multivariate gamma distributions is extensively used in the domains of stochastic modelling, reliability, hydrology and life testing. In this paper, we consider a multivariate gamma mixture model (MGMM) with independent marginals. A novel approach is proposed for estimating the parameters of this model. The approach makes use of Wilson-Hilferty approximation, MCLUST algorithm and the principle of maximum likelihood. Numerical illustrations based on simulated as well as real datasets have been implemented to assess the performance of the proposed approach. The results indicate that the proposed methodology provides reliable estimates for the model parameters.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite: Vaidyanathan, V.S. & Lakshmi, R.V., (2016). Estimation of parameters in a finite mixture of multivariate gamma distributions using gaussian approximation. Sri Lankan Journal of Applied Statistics. 17(3), pp.187–200. DOI: http://doi.org/10.4038/sljastats.v17i3.7902
Published on 30 Dec 2016.
Peer Reviewed

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