Fundamentals of Statistical Processing, Volume I: Estimation Theory by Steven M. KayFundamentals of Statistical Processing, Volume I: Estimation Theory by Steven M. Kay

Fundamentals of Statistical Processing, Volume I: Estimation Theory

bySteven M. Kay

Hardcover | March 26, 1993

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A unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms. 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.

Title:Fundamentals of Statistical Processing, Volume I: Estimation TheoryFormat:HardcoverDimensions:625 pages, 9.4 × 7.1 × 1 inPublished:March 26, 1993Publisher:Pearson EducationLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0133457117

ISBN - 13:9780133457117

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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.