Mixtures density estimation in lifetime data analysis: an application of nonparametric Bayesian estimation technique

Hossain, Ahmed and Khan, Hafiz (2010) Mixtures density estimation in lifetime data analysis: an application of nonparametric Bayesian estimation technique. Journal of Statistics & Management Systems, 13 (3). pp. 605-615. ISSN 0972-0510

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Abstract

In this paper we review a nonparametric Bayesian estimation technique in mixture of distributions employing a flexible Dirichlet process mixture. Methods for simulation based model fitting, in the presence of censoring, and for prior specification are provided. Using the method it allows dealing with a variety of practical issues including estimating density function, survival function, hazard function etc. Our interest on the other hand is to identify the underlying components of mixtures in a dataset by mixture model analysis. We thus illustrate our model with a simulated and a real data set under Type I censoring considering mixture of Weibull distributions. These illustrations demonstrate that modeling data in an infinite mixture works well when there are only a small finite number of components in the true mixtures.

Item Type: Article
Uncontrolled Keywords: Censored observations, kernel density, Dirichlet process, mixture models
Depositing User: Hafiz Khan
Date Deposited: 03 Feb 2011 11:04
Last Modified: 05 Sep 2017 15:19
URI: http://repository.uwl.ac.uk/id/eprint/3743

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