ISBN: 9780801894268 - regression estimators: a comparative study (20 risultati)

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Condizione: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condizione: New. With more than 150 exercises, Regression Estimators is a valuable resource for graduate students and professional statisticians. Num Pages: 424 pages, 5, 5 black & white line drawings. BIC Classification: PBT. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 229 x 152 x 28. Weight in Grams: 681. . 2010. hardcover. . . . . Books ship from the US and Ireland.…

Condizione: New. With more than 150 exercises, Regression Estimators is a valuable resource for graduate students and professional statisticians. Num Pages: 424 pages, 5, 5 black & white line drawings. BIC Classification: PBT. Category: (UP) Postgraduate, Research & Scholarly. Dimension: 229 x 152 x 28. Weight in Grams: 681. . 2010. hardcover. . . . .…

hardcover. Condizione: Very Good. no dust jacket as issued price sticker on flyleaf No dust jacket. Very Good hardcover with light shelfwear - NICE! Standard-sized.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

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Hardback. Condizione: New. second edition. An examination of mathematical formulations of ridge-regression-type estimators points to a curious observation: estimators can be derived by both Bayesian and Frequentist methods. In this updated and expanded edition of his 1990 treatise on the subject, Marvin H. J. Gruber presents, compares, and contrasts the development and properties of ridge-type estimators from these two philosophically different points of view. The book is organized into five sections. Part I gives a historical survey of the literature and summarizes basic ideas in matrix theory and statistical decision theory. Part II explores the mathematical relationships between estimators from both Bayesian and Frequentist points of view. Part III considers the efficiency of estimators with and without averaging over a prior distribution. Part IV applies the methods and results discussed in the previous two sections to the Kalman Filter, analysis of variance models, and penalized splines. Part V surveys recent developments in the field. These include efficiencies of ridge-type estimators for loss functions other than squared error loss functions and applications to information geometry.Gruber also includes an updated historical survey and bibliography. With more than 150 exercises, Regression Estimators is a valuable resource for graduate students and professional statisticians. …

Condizione: New. In English.

Condizione: New. pp. xii + 412 2nd Edition.

Condizione: New. pp. xii + 412 Illus.

Hardcover. Condizione: Brand New. 2nd edition. 384 pages. 8.90x6.10x1.10 inches. In Stock.

Gebunden. Condizione: New. With more than 150 exercises, Regression Estimators is a valuable resource for graduate students and professional statisticians.&Uumlber den AutorMarvin H. J. Gruber is a professor of mathematics and statistics at the Rochest.

Buch. Condizione: Neu. Neuware - An examination of mathematical formulations of ridge-regression-type estimators points to a curious observation: estimators can be derived by both Bayesian and Frequentist methods. In this updated and expanded edition of his 1990 treatise on the subject, Marvin H. J. Gruber presents, compares, and contrasts the development and properties of ridge-type estimators from these two philosophically different points of view. The book is organized into five sections. Part I gives a historical survey of the literature and summarizes basic ideas in matrix theory and statistical decision theory. Part II explores the mathematical relationships between estimators from both Bayesian and Frequentist points of view. Part III considers the efficiency of estimators with and without averaging over a prior distribution. Part IV applies the methods and results discussed in the previous two sections to the Kalman Filter, analysis of variance models, and penalized splines. Part V surveys recent developments in the field. These include efficiencies of ridge-type estimators for loss functions other than squared error loss functions and applications to information geometry. Gruber also includes an updated historical survey and bibliography. With more than 150 exercises, Regression Estimators is a valuable resource for graduate students and professional statisticians. …

Hardback. Condizione: New. second edition. An examination of mathematical formulations of ridge-regression-type estimators points to a curious observation: estimators can be derived by both Bayesian and Frequentist methods. In this updated and expanded edition of his 1990 treatise on the subject, Marvin H. J. Gruber presents, compares, and contrasts the development and properties of ridge-type estimators from these two philosophically different points of view. The book is organized into five sections. Part I gives a historical survey of the literature and summarizes basic ideas in matrix theory and statistical decision theory. Part II explores the mathematical relationships between estimators from both Bayesian and Frequentist points of view. Part III considers the efficiency of estimators with and without averaging over a prior distribution. Part IV applies the methods and results discussed in the previous two sections to the Kalman Filter, analysis of variance models, and penalized splines. Part V surveys recent developments in the field. These include efficiencies of ridge-type estimators for loss functions other than squared error loss functions and applications to information geometry.Gruber also includes an updated historical survey and bibliography. With more than 150 exercises, Regression Estimators is a valuable resource for graduate students and professional statisticians. …