UID:
almahu_9948234284702882
Format:
1 online resource (vi, 162 pages) :
,
digital, PDF file(s).
ISBN:
9780511791635 (ebook)
Content:
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.
Note:
Title from publisher's bibliographic system (viewed on 05 Oct 2015).
,
1. Introduction -- 2. Basics of coding -- 3. Basics of information -- 4. Modeling problems -- 5. Other optimality properties -- 6. Interval estimation -- 7. Hypothesis testing -- 8. Denoising -- 9. Sequential models -- Appendix A. Elements of algorithmic information -- Appendix B. Universal prior for integers.
Additional Edition:
Print version: ISBN 9781107004740
Language:
English
URL:
https://doi.org/10.1017/CBO9780511791635
