Fundamentals of Statistical Processing: Estimation Theory, Volume 1

by Steven Kay

Overview

A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. KEY TOPICS: Covers important approaches to obtaining an optimal estimator and analyzing its performance; and includes numerous examples as well as applications to real- world problems. MARKETS: For practicing engineers and scientists who design and analyze signal processing systems, i.e., to extract information from noisy signals — radar engineer, sonar engineer, geophysicist, oceanographer, biomedical engineer, communications engineer, economist, statistician, physicist, etc.

Product Details

ISBN-13:	9780133457117
Publisher:	Pearson Education
Publication date:	04/05/1993
Series:	Prentice Hall Signal Processing Series
Edition description:	New Edition
Pages:	608
Product dimensions:	7.25(w) x 9.50(h) x 1.12(d)

Table of Contents

1. Introduction.


2. Minimum Variance Unbiased Estimation.


3. Cramer-Rao Lower Bound.


4. Linear Models.


5. General Minimum Variance Unbiased Estimation.


6. Best Linear Unbiased Estimators.


7. Maximum Likelihood Estimation.


8. Least Squares.


9. Method of Moments.


10. The Bayesian Philosophy.


11. General Bayesian Estimators.


12. Linear Bayesian Estimators.


13. Kalman Filters.


14. Summary of Estimators.


15. Extension for Complex Data and Parameters.


Appendix: Review of Important Concepts.


Glossary of Symbols and Abbreviations.

Editorial Reviews

The outgrowth of a one-semester graduate level course on estimation theory given at the U. of Rhode Island, this text strikes a balance between the highly theoretical expositions written by statisticians and the practical treatments contributed by the many users of applied statistics. The primary focus is on obtaining optimal estimation algorithms that may be implemented on a digital computer. The background assumed is exposure to the basic theory of digital signal processing, probability and random processes, and linear and matrix algebra. Annotation c. Book News, Inc., Portland, OR (booknews.com)

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