American Journal of Applied Mathematics and Statistics
ISSN (Print): 2328-7306 ISSN (Online): 2328-7292 Website: http://www.sciepub.com/journal/ajams Editor-in-chief: Mohamed Seddeek
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American Journal of Applied Mathematics and Statistics. 2016, 4(4), 126-135
DOI: 10.12691/ajams-4-4-5
Open AccessArticle

Inference Based on Type II Progressively Interval Censored from Inverse Flexible Weibull Distribution Using Different Simulation Methods

W.M. Afify1,

1Head of Statistics, Mathematics & Insurance Department, Kafr El-sheikh University, Faculty of Commerce

Pub. Date: September 01, 2016

Cite this paper:
W.M. Afify. Inference Based on Type II Progressively Interval Censored from Inverse Flexible Weibull Distribution Using Different Simulation Methods. American Journal of Applied Mathematics and Statistics. 2016; 4(4):126-135. doi: 10.12691/ajams-4-4-5

Abstract

This paper considers the estimation problem for inverse flexible Weibull model, when the lifetimes are collected under type-II progressive interval censoring. The maximum likelihood and the Bayes estimators for the two unknown parameters of the inverse flexible Weibull distribution are derived. Point estimation and confidence intervals based on maximum likelihood and bootstrap method are also proposed. Bayesian estimation for population parameter under type-II progressive interval censoring is studied via Markov Chain Monte Carlo (MCMC) simulation. To illustrate the proposed methods will discuss an example with the real data. Finally, comparing the two techniques through comparisons between the maximum likelihood using Monte Carlo simulation and bootstrap method on the one hand, and comparing them with the Bayes estimators using MCMC study on the other hand.

Keywords:
Inverse flexible Weibull distribution progressive interval type-II censoring bootstrap-t Algorithm Bayesian and non-Bayesian approach MCMC

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