An Application of EM Test for the Bayesian Change Point Problem
-
A. M. Variyath
and
C. V. Vasudevan
Abstract
In any manufacturing process, identification of changes in the process conditions is of great interest. Recently, a Bayesian approach for the identification of the change in process mean was proposed assuming that the response of interest follow an exponential family distribution. In this approach, the expectation – maximization (EM) algorithm was used for estimating the process parameters. In general, the EM algorithm is computationally intensive and the optimality depends on the initial values of the parameters chosen. We extend the idea of the EM test for homogeneity to extend this Bayesian approach to the change point problem. Our simulations studies show that the developed EM test procedure converges at a faster rate than the original EM approach. Our studies also show that the EM test with binomial prior distribution leads to solutions very close to the true values. We have applied our approach to two case examples.
© 2013 by Walter de Gruyter Berlin Boston
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Articles in the same Issue
- Masthead
- Masthead
- The Kumaraswamy Exponentiated Pareto Distribution
- Sequential Test for Poisson Distribution under Measurement Error
- Measurement Error Effect on the Power of Control Chart for the Ratio of Two Poisson Distributions
- The Bivariate Confluent Hypergeometric Series Distribution and Some of Its Properties
- Availability of a k-out-of-N:F System with Generally Distributed Repair Time and Preventive Maintenance
- Empirical Likelihood Based Control Charts
- Reliable Risk Analysis on the Example of Tsunami Heights
- An Application of EM Test for the Bayesian Change Point Problem
