Estimation of parameters from progressively censored data using EM algorithm

• Published in: Computational statistics & data analysis Vol. 39; no. 4; pp. 371 - 386
• Main Authors: Ng, H.K.T., Chan, P.S., Balakrishnan, N.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 28.06.2002; Elsevier Science; Elsevier
• Series: Computational Statistics & Data Analysis
• Subjects: Asymptotic variances; EM algorithm; Exact sciences and technology; Lifetime data; Linear inference, regression; Mathematical foundations; Mathematics; Maximum likelihood estimators; Missing information; Nonparametric inference; Parametric inference; Probability and statistics; Sciences and techniques of general use; Statistics; Type-II progressive censoring; Maximum likelihood estimators; Lifetime data; Missing information; EM algorithm; Asymptotic variances; Type-II progressive censoring; Censored data; Parameter estimation; Covariance analysis; Asymptotic behavior; Gamma function; Asymptotic variance; Variance analysis; Implementation; Lognormal distribution; Newton Raphson method; Variance; Statistical regression; Hypothesis test; Statistical test; Lifetime; Covariance; Distribution function; Asymptotic approximation; Maximum likelihood; Weibull distribution; Weibull lifetime data
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/S0167-9473(01)00091-3

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.