Jump to ContentJump to Main Navigation
Numerical Methods for Nonlinear Estimating Equations
Users without a subscription are not able to see the full content.

Numerical Methods for Nonlinear Estimating Equations

Christopher G. Small and Jinfang Wang

Abstract

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. Root search algorithms and one-step estimators are standard methods 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 converg ... More

Keywords: artificial likelihood, Bayesian estimating functions, dynamical system, iterative algorithm, multiple roots, quasi-likelihood, root selection, semiparametric model, statistical inference

Bibliographic Information

Print publication date: 2003 Print ISBN-13: 9780198506881
Published to Oxford Scholarship Online: September 2007 DOI:10.1093/acprof:oso/9780198506881.001.0001

Authors

Affiliations are at time of print publication.

Christopher G. Small, author
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada

Jinfang Wang, author
School of Agriculture, Obihiro University of Agriculture and Veterinary Medicine, Inada-cho, Obihiro, Hokkaido, Japan

Subscriber Login

Forgotten your password?