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

Heyde, Christopher C.

ISBN 10: 0387982256 / ISBN 13: 9780387982250

Editore: Springer, 1997

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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.

Codice articolo 9741519

Dati bibliografici

• Titolo: Quasi-Likelihood and Its Application: A General Approach to Optimal Parameter Estimation
• Editore: Springer
• Data di pubblicazione: 1997
• Lingua: Inglese
• ISBN 10: 0387982256
• ISBN 13: 9780387982250
• Rilegatura: Rilegato
• Condizione: Good
• Peso: 550 grammi
• Cataloghi dei venditori:
  • Mathematics

Descrizione articolo

Riassunto

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.

Contenuti

The General Framework.- An Alternative Approach: E-Sufficiency.- Asymptotic Confidence Zones of Minimum Size.- Asymptotic Quasi-Likelihood.- Combining Estimating Functions.- Projected Quasi-Likelihood.- Bypassing the Likelihood.- Hypothesis Testing.- Infinite Dimensional Problems.- Miscellaneous Applications.- Consistency and Asymptotic Normality for Estimating Functions.- Complements and Strategies for Application.