Estimation of parameters from progressively censored data using EM algorithm

Authors:
H. K. T. Ng;P. S. Chan;N. Balakrishnan
Affiliations:
Department of Mathematics and Statistics McMaster University, Hamilton, Ontario, Canada;Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong;Department of Mathematics and Statistics McMaster University, Hamilton, Ontario, Canada
Venue:
Computational Statistics & Data Analysis
Year:
2002

Citing 1
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Abstract

EM algorithm is used to determine the maximum likelihood estimates when the data are progressively Type II censored. The method is shown to be feasible and easy to implement. The asymptotic variances and covariances of the ML estimates are computed by means of the missing information principle. The methodology is illustrated with two popular models in lifetime analysis, the lognormal and Weibull lifetime distributions.