Overview
Nonlinearity arises in statistical inference in various ways, with varying degrees of severity, as an obstacle to statistical analysis. More entrenched forms of nonlinearity often require intensive numerical methods to construct estimators, and the use of root search algorithms, or one-step estimators, is a standard method of solution. This book provides a comprehensive study of nonlinear estimating equations and artificial likelihoods for statistical inference. It provides extensive coverage and comparison of hill climbing algorithms, which, when started at points of nonconcavity often have very poor convergence properties, and for additional flexibility proposes a numberof modifications to the standard methods for solving these algorithms. The book also extends beyond simple root search algorithms to include a discussion of the testing of roots for consistency, and the modification of available estimating functions to provide greater stability in inference. A variety of examples from practical applications are included to illustrate the problems and possibilities thus making this text ideal for the research statistician and graduate student.This is the latest in the well-established and authoritative Oxford Statistical Science Series, which includes texts and monographs covering many topics of current research interest in pure and applied statistics. Each title has an original slant even if the material included is not specifically original. The authors are leading researchers and the topics covered will be of interest to all professional statisticians, whether they be in industry, government department or research institute. Other books in the series include 23. W.J.Krzanowski: Principles of multivariate analysis: a user's perspective updated edition 24. J.Durbin and S.J.Koopman: Time series analysis by State Space Models 25. Peter J. Diggle, Patrick Heagerty, Kung-Yee Liang, Scott L. Zeger: Analysis of Longitudinal Data 2/e 26. J.K. Lindsey: Nonlinear Models in Medical Statistics 27. Peter J. Green, Nils L. Hjort & Sylvia Richardson: Highly Structured Stochastic Systems 28. Margaret S. Pepe: The Statistical Evaluation of Medical Tests for Classification and Prediction
Full Product Details
Author: Christopher G. Small (, Department of Statistics and Actuarial Science University of Waterloo Waterloo, Ontario N2L 3G1 Canada) ,
Jinfang Wang (, School of Agriculture Obihiro University of Agriculture and Veterinary Medicine Inada-cho, Obihiro, Hokkaido 080-8555, Japan)
Publisher: Oxford University Press
Imprint: Oxford University Press
Volume: 29
Dimensions:
Width: 16.10cm
, Height: 2.10cm
, Length: 24.10cm
Weight: 0.700kg
ISBN: 9780198506881
ISBN 10: 0198506880
Pages: 324
Publication Date: 02 October 2003
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: To order
Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us.
Reviews
This book provides a comprehensive study of the solution of non-linear estimating equations arising in statistical inference. Mathematical Reviews
Author Information
Christopher G. Small is a Professor of Statistics at the University of Waterloo, Canada, and has been Canada's official representative and Team Leader for the International Mathematical Olympiad in Taiwan (1998) and Washington (2000). Jinfang Wang is an Associate Professor in Obihiro University.
