Quasi-Likelihood And Its Application

Heyde, Christopher C. (Australian National University, Canberra, Australia)

ISBN 10: 1475771045 ISBN 13: 9781475771046

Verlag: Springer-Verlag New York Inc., 2013

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Beschreibung

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. Bestandsnummer des Verk�ufers V9781475771046

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Inhaltsangabe:

This important book in statistical theory by one of the leading experts in this area unifies the two two important approaches to statistical parameter estimation. It will be of interest to researchers and graduate students in both mathematical statistics and probability theory.

Rese�a del editor: The first account in book form of all the essential features of the quasi-likelihood methodology, stressing its value as a general purpose inferential tool. The treatment is rather informal, emphasizing essential principles rather than detailed proofs, and readers are assumed to have a firm grounding in probability and statistics at the graduate level. Many examples of the use of the methods in both classical statistical and stochastic process contexts are provided.

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Bibliografische Details

Titel: Quasi-Likelihood And Its Application
Verlag: Springer-Verlag New York Inc.
Erscheinungsdatum: 2013
Einband: Softcover
Zustand: New

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Quasi-Likelihood And Its Application : A General Approach to Optimal Parameter Estimation

Christopher C. Heyde

Verlag: Springer, Springer, 2013

ISBN 10: 1475771045 ISBN 13: 9781475771046

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Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland

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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. Artikel-Nr. 9781475771046

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