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EM Algorithm and Optimal Censoring Schemes for Progressively Type-II Censored Bivariate Normal Data

N. Balakrishnan, J.-A. Kim

Open publisher page 11 citations

Abstract

The EM algorithm is used to find the maximum likelihood estimates (MLEs) of the parameters of a bivariate normal distribution based on progressively Type-II right censored samples. The asymptotic variances and covariances of the MLEs are derived, using the missing information principle, from the Fisher information matrix as well as from the partially observed information matrix. Optimal censoring schemes are then investigated with respect to minimum trace of the variance-covariance matrix of the MLEs and also with respect to the maximum information about ρ .

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What this paper is about

The EM algorithm is used to find the maximum likelihood estimates (MLEs) of the parameters of a bivariate normal distribution based on progressively Type-II right censored samples. The asymptotic variances and covariances of the MLEs are derived, using the missing information principle, from the Fisher information matrix as well as from the partially observed information matrix. Optimal censoring schemes are then investigated with respect to minimum trace of the variance-covariance matrix of the MLEs and also with respect to the maximum information about ρ .

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OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The EM algorithm is used to find the maximum likelihood estimates (MLEs) of the parameters of a bivariate normal distribution based on progressively Type-II right censored samples. The asymptotic variances and covariances of the MLEs are derived, using the missing information principle, from the Fisher information matrix as well as from the partially observed information matrix. Optimal censoring schemes are then investigated with respect to minimum trace of the variance-covariance matrix of the MLEs and also with respect to the maximum information about ρ .

Key concepts: Censoring (clinical trials), Mathematics, Fisher information, Statistics, Bivariate analysis, Covariance matrix, Multivariate normal distribution, Maximum likelihood

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