Optimal Estimation of Parameters

This book presents a comprehensive and consistent theory of estimation. The framework described leads naturally to a powerful new tool, the generalized maximum capacity estimator. This approach allows the optimal estimation of real-valued parameters, their number and intervals, and provides common ground for explaining the power of these estimators.

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This book presents a comprehensive and consistent theory of estimation. The framework described leads naturally to a generalized maximum capacity estimator. This approach allows the optimal estimation of real-valued parameters, their number and intervals, as well as providing common ground for explaining the power of these estimators. Beginning with a review of coding and the key properties of information, the author goes on to discuss the techniques of estimation and develops the generalized maximum capacity estimator, based on a new form of Shannon's mutual information and channel capacity. Applications of this powerful technique in hypothesis testing and denoising are described in detail. Offering an original and thought-provoking perspective on estimation theory, Jorma Rissanen's book is of interest to graduate students and researchers in the fields of information theory, probability and statistics, econometrics and finance.