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

Christopher C. Heyde

ISBN 10: 1475771045 / ISBN 13: 9781475771046

Published by Springer, Springer, 2013

About this Item

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.

Seller Inventory # 9781475771046

Bibliographic Details

• Title: Quasi-Likelihood And Its Application : A General Approach to Optimal Parameter Estimation
• Publisher: Springer, Springer
• Publication Date: 2013
• Language: English
• ISBN 10: 1475771045
• ISBN 13: 9781475771046
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 14D millimeters 235 millimeters width by 155 millimeters height by 14 millimeters depth
• Weight: 388 grams

About this title

Synopsis

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.