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Fundamentals of Statistical Processing Estimation Theory, Volume 1

Kay Steven
Innbundet / 1993 / Engelsk

Produktdetaljer

ISBN
9780133457117
Publisert
1993-05-08
Utgiver
Pearson Education (US)
Vekt
1040 gr
Høyde
238 mm
Bredde
180 mm
Dybde
28 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
608

Forfatter
Kay Steven

Fundamentals of Statistical Processing Estimation Theory, Volume 1

Kay Steven
Innbundet / 1993 / Engelsk
Kay Steven
Innbundet / 1993 / Engelsk
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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.
Les mer
Intended for practicing engineers and scientists who design and analyze signal processing systems. This work offers a unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.
Les mer


 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.
Les mer
  • 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.
Les mer

Relaterte produkter

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.
Les mer
Intended for practicing engineers and scientists who design and analyze signal processing systems. This work offers a unified presentation of parameter estimation for those involved in the design and implementation of statistical signal processing algorithms.
Les mer


 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.
Les mer
  • 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.
Les mer

Produktdetaljer

ISBN
9780133457117
Publisert
1993-05-08
Utgiver
Pearson Education (US)
Vekt
1040 gr
Høyde
238 mm
Bredde
180 mm
Dybde
28 mm
Aldersnivå
U, 05
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
608

Forfatter
Kay Steven

Relaterte produkter