Mixtures density estimation in lifetime data analysis: an application of nonparametric Bayesian estimation technique
Hossain, Ahmed and Khan, Hafiz T. A. (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
Full text is not in this repository.
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.
|Research Areas:||A. > School of Law > Criminology and Sociology|
|Depositing User:||Ms Jyoti Zade|
|Date Deposited:||03 Feb 2011 11:04|
|Last Modified:||20 Mar 2017 11:39|
Actions (login required)