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SCIDIR_ocn898772151 |
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OCoLC |
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20231117044505.0 |
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m o d |
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cr cnu---unuuu |
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141227s1990 mau ob 001 0 eng d |
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|a EBLCP
|b eng
|e pn
|c EBLCP
|d N$T
|d OCLCQ
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|d OCLCQ
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|a 622222994
|a 680284066
|a 1100857981
|a 1162559681
|a 1397684403
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|a 9781483260976
|q (electronic bk.)
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|a 1483260976
|q (electronic bk.)
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|z 9780123047526
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|z 0123047528
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|a (OCoLC)898772151
|z (OCoLC)622222994
|z (OCoLC)680284066
|z (OCoLC)1100857981
|z (OCoLC)1162559681
|z (OCoLC)1397684403
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|a dlr
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|a QA278.2 .G78 2014
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|a MAT
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|a MAT
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|a 519.5/44
|a 519.544
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|a Gruber, Marvin H. J.,
|d 1941-
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|a Regression estimators :
|b a comparative study /
|c Marvin H.J. Gruber.
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| 260 |
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|a Boston :
|b Academic Press,
|c �1990.
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| 300 |
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|a 1 online resource (xi, 347 pages)
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Statistical modeling and decision science
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| 504 |
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|a Includes bibliographical references (pages 327-334) and index.
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|3 Use copy
|f Restrictions unspecified
|2 star
|5 MiAaHDL
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| 533 |
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|a Electronic reproduction.
|b [Place of publication not identified] :
|c HathiTrust Digital Library,
|d 2010.
|5 MiAaHDL
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| 538 |
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|a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
|u http://purl.oclc.org/DLF/benchrepro0212
|5 MiAaHDL
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| 583 |
1 |
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|a digitized
|c 2010
|h HathiTrust Digital Library
|l committed to preserve
|2 pda
|5 MiAaHDL
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|a Print version record.
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|a Front Cover; Regression Estimators: A Comparative Study; Copyright Page; Table of Contents; Preface; Part I: Introduction and Mathematical Preliminaries; Chapter I. Introduction; 1.0. Motivation for Writing This Book; 1.1. Purpose of This Book; 1.2. Least Square Estimators and the Need for Alternatives; 1.3. Historical Survey; 1.4. The Structure of the Book; Chapter II. Mathematical and Statistical Preliminaries; 2.0. Introduction; 2.1. Matrix Theory Results; 2.2. The Bayes Estimator; 2.3. The Minimax Estimator; 2.4. Criterion for Comparing Estimators: Theobald's1974 Result.
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|a 2.5. Some Useful Inequalities2.6. Some Miscellaneous Useful Matrix Results; 2.7. Summary; Part II: The Estimators; Chapter III. The Estimators; 3.0. Introduction; 3.1. The Least Square Estimator and Its Properties; 3.2. The Generalized Ridge Regression Estimator; 3.3. The Mixed Estimators; 3.4. The Linear Minimax Estimator; 3.5. The Bayes Estimator; 3.6. Summary and Remarks; Chapter IV. How the Different Estimators Are Related; 4.0. Introduction; 4.1. Alternative Forms of the Bayes Estimator Full Rank Case; 4.2. Alternative Forms of the Bayes Estimator Non-FullRank Case.
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|a 4.3. The Equivalence of the Generalized Ridge Estimatorand the Bayes Estimator4.4. The Equivalence of the Mixed Estimatorand the BayesEstimator; 4.5. Ridge Estimators in the Literature as Special Cases ofthe BE, Minimax Estimators, or Mixed Estimators; 4.6. Extension of Results to the Case where U'FU Is Not PositiveDefinite; 4.7. An Extension of the Gauss-Markov Theorem; 4.8. Summary and Remarks; Part III: The Efficiencies of the Estimators; Chapter V. Measures of Efficiency of the Estimators; Chapter VI. The Average MSE; 6.0. Introduction.
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|a 6.1. The Forms of the MSE for the Minimax, Bayes andthe Mixed Estimator6.2. Relationship Between the Average Variance and theMSE; 6.3. The Average Variance and the MSE of the BE; 6.4. Alternative Forms of the MSE of the Mixed Estimator; 6.5. Comparison of the MSE of Different BE; 6.6. Comparison of the Ridge and Contraction Estimator'sMSE; 6.7. Summary and Remarks; Chapter VII. The MSE Neglecting the Prior Assumptions; 7.0. Introduction; 7.1. The MSE of the BE; 7.2. The MSE of the Mixed Estimators Neglecting the Prior Assumptions.
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|a 7.3. The Comparison of the Conditional MSE of the Bayes Estimator and the Least Square Estimator and the Comparison of the Conditional and the AverageMSE7.4. The Comparison of the MSE of a Mixed Estimatorwith the LS Estimators; 7.5. The Comparison of the MSE of Two BE; 7.6. Summary; Chapter VIII. The MSE for Incorrect Prior Assumptions; 8.0. Introductio; 8.1. The BE and Its MSE; 8.2. The Minimax Estimator; 8.3. The Mixed Estimator; 8.4. Contaminated Priors; 8.5. Contaminated (Mixed) Bayes Estimators; 8.6. Summary; Part IV: Applications; Chapter IX. The Kaiman Filter; 9.0. Introduction; 9.1. The Kaiman Filter as a BayesEstimator.
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| 546 |
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|a English.
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| 650 |
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0 |
|a Ridge regression (Statistics)
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| 650 |
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0 |
|a Estimation theory.
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| 650 |
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6 |
|a R�egression pseudo-orthogonale.
|0 (CaQQLa)000260900
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| 650 |
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6 |
|a Th�eorie de l'estimation.
|0 (CaQQLa)201-0007579
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| 650 |
|
7 |
|a MATHEMATICS
|x Applied.
|2 bisacsh
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| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Estimation theory
|2 fast
|0 (OCoLC)fst00915531
|
| 650 |
|
7 |
|a Ridge regression (Statistics)
|2 fast
|0 (OCoLC)fst01097769
|
| 776 |
0 |
8 |
|i Print version:
|a Gruber, Marvin H.J., 1941-
|t Regression estimators.
|d Boston : Academic Press, �1990
|z 0123047528
|w (DLC) 89029740
|w (OCoLC)20670251
|
| 830 |
|
0 |
|a Statistical modeling and decision science.
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780123047526
|z Texto completo
|
