Order Statistics and Inference: Estimation Methods
The literature on order statistics and inference is quite extensive and covers a large number of fields, but most of it is dispersed throughout numerous publications. This volume is the consolidation of the most important results and places an emphasis on estimation. Both theoretical and computational procedures are presented to meet the needs of researchers, professionals, and students. The methods of estimation discussed are well-illustrated with numerous practical examples from both the physical and life sciences, including sociology, psychology, and electrical and chemical engineering. A complete, comprehensive bibliography is included so the book can be used both as a text and reference.
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Moments and Other Expected Values
Linear Estimation Based on Order Statistics
Maximum Likelihood Estimation
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AMLEs applications approximate assumed asymptotically Balakrishnan becomes best linear Bias BLUE calculate censored sample Chan Chapter coefficients Cohen and Whitten computed conditional corresponding denote density function derived detail determined efficient employed equations evaluate Example exponential distribution expression extreme failure formulas given gives Gupta Harter hence identity integral interest inverse iterative joint largest Let us consider linear estimators logistic distribution matrix maximum likelihood estimators mean mentioned method modified n-Sin normal distribution observations obtain optimal order statistics parameter pointed population presented problems product moments random sample Rayleigh distribution relation relative respectively sample sizes scale parameters selected setting simple simplified simulated single solving standard standard errors symmetric Table tabulated Theorem tion truncated Type Type-II censored sample unbiased estimators uniform values variances and covariance various Weibull Xi:n Xn:n yields