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  3. Fundamentals of Statistical Processing, Volume I: Estimation Theory

Fundamentals of Statistical Processing, Volume I: Estimation Theory, 1st edition

  • Steven M. Kay

Published by Prentice Hall (March 26th 1993) - Copyright © 1993

1st edition

Fundamentals of Statistical Processing, Volume I: Estimation Theory

ISBN-13: 9780133457117

Includes: Hardcover
Free delivery
$137.00

What's included

  • Hardcover

    You'll get a bound printed text.

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.

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.

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