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

Published by Pearson (March 26, 1993) © 1993

  • Steven M. Kay University of Rhode Island

  • A print text (hardcover or paperback) 
  • Free shipping
  • Also available for purchase as an ebook from all major ebook resellers, including

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.

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

  • describes the field of parameter estimation based on time series data.
  • provides a summary of principal approaches as well as a “roadmap” to use in the selection of an estimator.
  • extends many of the results for real data/real parameters to complex data/complex parameters.
  • summarizes as examples many of the important estimators used in practice.
  • illustrates how a digital computer can be used to assess performance of an estimator.
  • emphasizes a linear model to allow an optimal estimator to be found by inspection of a data model.

 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.

Need help? Get in touch

Privacy and cookies
By watching, you agree Pearson can share your viewership data for marketing and analytics for one year, revocable by deleting your cookies.

Pearson eTextbook: What’s on the inside just might surprise you

They say you can’t judge a book by its cover. It’s the same with your students. Meet each one right where they are with an engaging, interactive, personalized learning experience that goes beyond the textbook to fit any schedule, any budget, and any lifestyle.