Description
This seminal work by Constance van Eeden provides an in-depth treatment of estimation problems where parameters are restricted to bounded or constrained spaces. The text addresses fundamental challenges in statistical theory when standard estimation procedures must be adapted to respect parameter constraints.
Van Eeden systematically explores methodologies for handling restricted parameter spaces, including theoretical foundations and practical applications. The book covers key concepts such as maximum likelihood estimation under constraints, admissibility of estimators, and minimax approaches. It examines various statistical distributions and demonstrates how classical estimation theory must be modified when parameters lie within specific regions.
Essential for statisticians, researchers, and graduate students working in theoretical statistics and constrained optimization problems, this Springer-Verlag publication serves as both a reference and comprehensive guide to one of statistics’ most challenging domains.
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