heyde christopher c (15 Ergebnisse)

Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 62 HEY 9780387226798 Sprache: Englisch Gewicht in Gramm: 550.

Zustand: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,550grams, ISBN:9780387982250.

Zustand: New. pp. 252 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Kartoniert. Zustand: Gut. 24 cm XII, 500 S. Orig.-Karton. Mit Abbildungen im Text. Gutes Exemplar.

Hardcover/ Pappband. Zustand: Gut. 172 S. Statistics Statistik Cover slightly damaged of a removed label. Guter Zustand/ Good With ill. Ex-Library. ha1064071 Sprache: Englisch Gewicht in Gramm: 600.

Zustand: New. *Price HAS BEEN REDUCED by 10% until Monday, Oct. 13 (sale item)* first edition, first printing; 235 pp., hardcover, NEW!! - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Buch. Zustand: Neu. Neuware -This book is concerned with the general theory of optimal estimation of - rameters in systems subject to random e ects and with the application of this theory. The focus is on choice of families of estimating functions, rather than the estimators derived therefrom, and on optimization within these families. Only assumptions about means and covariances are required for an initial d- cussion. Nevertheless, the theory that is developed mimics that of maximum likelihood, at least to the rst order of asymptotics. The term quasi-likelihood has often had a narrow interpretation, asso- ated with its application to generalized linear model type contexts, while that of optimal estimating functions has embraced a broader concept. There is, however, no essential distinction between the underlying ideas and the term quasi-likelihood has herein been adopted as the general label. This emphasizes its role in extension of likelihood based theory. The idea throughout involves nding quasi-scores from families of estimating functions. Then, the qua- likelihood estimator is derived from the quasi-score by equating to zero and solving, just as the maximum likelihood estimator is derived from the like- hood score.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 252 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is concerned with the general theory of optimal estimation of - rameters in systems subject to random e ects and with the application of this theory. The focus is on choice of families of estimating functions, rather than the estimators derived therefrom, and on optimization within these families. Only assumptions about means and covariances are required for an initial d- cussion. Nevertheless, the theory that is developed mimics that of maximum likelihood, at least to the rst order of asymptotics. The term quasi-likelihood has often had a narrow interpretation, asso- ated with its application to generalized linear model type contexts, while that of optimal estimating functions has embraced a broader concept. There is, however, no essential distinction between the underlying ideas and the term quasi-likelihood has herein been adopted as the general label. This emphasizes its role in extension of likelihood based theory. The idea throughout involves nding quasi-scores from families of estimating functions. Then, the qua- likelihood estimator is derived from the quasi-score by equating to zero and solving, just as the maximum likelihood estimator is derived from the like- hood score.

Zustand: New. In.

Zustand: New. In.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is concerned with the general theory of optimal estimation of - rameters in systems subject to random e ects and with the application of this theory. The focus is on choice of families of estimating functions, rather than the estimators derived therefrom, and on optimization within these families. Only assumptions about means and covariances are required for an initial d- cussion. Nevertheless, the theory that is developed mimics that of maximum likelihood, at least to the rst order of asymptotics. The term quasi-likelihood has often had a narrow interpretation, asso- ated with its application to generalized linear model type contexts, while that of optimal estimating functions has embraced a broader concept. There is, however, no essential distinction between the underlying ideas and the term quasi-likelihood has herein been adopted as the general label. This emphasizes its role in extension of likelihood based theory. The idea throughout involves nding quasi-scores from families of estimating functions. Then, the qua- likelihood estimator is derived from the quasi-score by equating to zero and solving, just as the maximum likelihood estimator is derived from the like- hood score.

Paperback. Zustand: Brand New. 246 pages. 9.25x6.10x0.57 inches. In Stock.

Zustand: New. Series: Springer Series in Statistics. Num Pages: 246 pages, biography. BIC Classification: PBW. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 13. Weight in Grams: 391. . 2013. Softcover reprint of the original 1st ed. 1997. Paperback. . . . . Books ship from the US and Ireland.