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Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.
List of contents
Introduction to Likelihood. Profile Likelihood. Marginal and Conditional Likelihood. Likelihood inference and model misspeciation. Likelihood Inference for some complex models. Quasi-likelihood and Estimating Equations. Composite Likelihood. Semi-parametric and Partial Likelihood. Empirical Likelihood. Penalized Likelihood and Semi-parametric Models. Local Likelihood and Nonparametric Smoothing. Special Topics. Computation.
About the author
Nancy Reid is a University Professor and Canada Research Chair in the Department of Statistical Sciences at the University of Toronto, Canada
Cristiano Varin is an Assistant Professor in the Department of Informatics and Statistics at Ca Foscari University, Venice, Italy
Grace Yi is a Professor and University Research Chair in the Department of Statistics and Actuarial Science at the University of Waterloo, Canda
Summary
Significant new challenges to the use of likelihood-based methods for inference have helped to generate considerable interest in alternative inference methods that are not based on a full likelihood specification. This book provides a comprehensive survey of likelihood methods in statistics, with an emphasis on developments to inference functions for use in complex data. These inference functions are usually motivated by considerations related to likelihood-type arguments and have a variety of names, including composite likelihood, quasi-likelihood and pseudo-likelihood.
