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

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Advances in Ranking and Selection, Multiple Comparisons, and Reliability

Part of the book series: Statistics for Industry and Technology ((SIT))

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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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada

    N. Balakrishnan & J. -A. Kim

Authors
  1. N. Balakrishnan
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  2. J. -A. Kim
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, L8S 4K1, Canada

    N. Balakrishnan

  2. Department of Statistics, Ohio State University, 19S8 Neil Avenue Cockins Hall, Room 404, Columbus, OH, 43210-1247, USA

    H. N. Nagaraja

  3. Department of Management Science and Statistics, University of Texas at San Antonio, 6900 N. Loop 1604 W, San Antonio, TX, 78249-0632, USA

    N. Kannan

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© 2005 Birkhäuser Boston

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Balakrishnan, N., Kim, J.A. (2005). EM Algorithm and Optimal Censoring Schemes for Progressively Type-II Censored Bivariate Normal Data. In: Balakrishnan, N., Nagaraja, H.N., Kannan, N. (eds) Advances in Ranking and Selection, Multiple Comparisons, and Reliability. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/0-8176-4422-9_2

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