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ISSN: 2168-9679

Journal of Applied & Computational Mathematics
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Research Article

Reliability Analysis for Monte Carlo Simulation Using the Expectation- Maximization Algorithm for a Weibull Mixture Distribution Model

Emad E. Elmahdy*

Department of Mathematics, Science College, King Saud University, Riyadh 11451, P.O. 2455, Saudi Arabia

*Corresponding Author:
Emad E. Elmahdy
Department of Mathematics, Science College
King Saud University, Riyadh 11451, P.O. 2455, Saudi Arabia
Tel: 00966508683753
E-mail: [email protected]

Received date: May 20, 2016; Accepted date: May 20, 2016; Published date: June 27, 2016

Citation: Emad E. Elmahdy (2016) Reliability Analysis for Monte Carlo Simulation Using the Expectation-Maximization Algorithm for a Weibull Mixture Distribution Model. J Appl Computat Math 5:310. doi:10.4172/2168-9679.1000310

Copyright: © 2016 Emad E. Elmahdy. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

This paper presents a simulation study of a finite Weibull mixture distribution (WMD) for modelling life data related to system components with different failure modes. The main aim of this study is to compare two analytical methods for estimating the parameters of WMD models, the maximum likelihood estimation (MLE) method using the expectation-maximization (EM) algorithm, [A1] and the non-linear median rank regression (NLMRR) method with the Levenberg-Marquardt algorithm. To perform this comparison, the Monte Carlo simulation technique is implemented to generate several replicates for complete failure data and censored data based on samples of different sizes that follow a two-component WMD. This study showed that MLE using the EM algorithm yields more accurate parameter estimates than the NLMRR method for small or moderate complete failure data samples. This method also converges faster than the NLMRR method for large samples that include censored data.

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