ASYMPTOTIC PROPERTIES OF MLE FOR WEIBULLDISTRIBUTION WITH GROUPED DATA
Pages: 176-186
Year: 2002 Issue: 2
Journal: Journal of Systems Science and Complexity
Keyword: GetLinkList(KeywordFilter('Grouped data; MLE; Weibull distribution; identifiable; strongly consistent; asymptotically normal; the law of iterated logarithm; Newton iteration arithmetic.'); 'kw'; 'CJFQ');
Abstract: Abstract. A grouped data model for Weibull distribution is considered. Under mild con-ditions, the maximum likelihood estimators(MLE) are shown to be identifiable, stronglyconsistent, asymptotically normal, and satisfy the law of iterated logarithm. Newton iter-ation algorithm is also considered, which converges to the unique solution of the likelihoodequation. Moreover, we extend these results to a random case.
Year: 2002 Issue: 2
Journal: Journal of Systems Science and Complexity
Keyword: GetLinkList(KeywordFilter('Grouped data; MLE; Weibull distribution; identifiable; strongly consistent; asymptotically normal; the law of iterated logarithm; Newton iteration arithmetic.'); 'kw'; 'CJFQ');
Abstract: Abstract. A grouped data model for Weibull distribution is considered. Under mild con-ditions, the maximum likelihood estimators(MLE) are shown to be identifiable, stronglyconsistent, asymptotically normal, and satisfy the law of iterated logarithm. Newton iter-ation algorithm is also considered, which converges to the unique solution of the likelihoodequation. Moreover, we extend these results to a random case.
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