Introduction: Some history and some examples; A statement of the problem, the notation and some definitions; (In)admissibility and dominators; Minimax estimators and their admissibility; Presence of nuisance parameters; The linear model; Other properties; Existence of MLEs and algorithms to compute them
Summary
Contains a review of 50 years of results on questions of admissibility and minimaxity of estimators of parameters that are restricted to closed convex subsets of Rk. This book gives an overview of known algorithms for computing maximum likelihood estimators under order-restrictions. It is intended for researchers and graduate students
Bibliography
Includes bibliographical references (pages 143-159)-and indexes
