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2014•Journal of Anhui Normal UniversityRequires access

The EM Algorithm for the Estimation of Parameters Under the General Type-II Progressive Censoring Data

Wang Jua

Open publisher page 1 citations

Abstract

The general Type-Ⅱprogressive censoring data is an important way for getting the life time data.It is often difficult to obtain the maximum-likelihood estimation.This paper stressed on the lognormal model of the sample space under the general Type-Ⅱprogressive censoring and got the MLE by the EM Algorithm.A computational simulation and comparison is done and the estimates are the same as the MLEs from the Newton-Raphson algorithm and more efficient.

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What this paper is about

The general Type-Ⅱprogressive censoring data is an important way for getting the life time data.It is often difficult to obtain the maximum-likelihood estimation.This paper stressed on the lognormal model of the sample space under the general Type-Ⅱprogressive censoring and got the MLE by the EM Algorithm.A computational simulation and comparison is done and the estimates are the same as the MLEs from the Newton-Raphson algorithm and more efficient.

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Available abstract

The general Type-Ⅱprogressive censoring data is an important way for getting the life time data.It is often difficult to obtain the maximum-likelihood estimation.This paper stressed on the lognormal model of the sample space under the general Type-Ⅱprogressive censoring and got the MLE by the EM Algorithm.A computational simulation and comparison is done and the estimates are the same as the MLEs from the Newton-Raphson algorithm and more efficient.

Key concepts: Censoring (clinical trials), Maximum likelihood, Expectation–maximization algorithm, Computer science, Mathematics, Statistics, Algorithm, Applied mathematics

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