Measurement Error in Nonlinear Models : A Modern Perspective

Carroll, Raymond J. (EDT); Ruppert, David; Stefanski, Leonard A.; Crainiceanu, Ciprian M.

ISBN 10: 1584886331 / ISBN 13: 9781584886334

Published by Chapman and Hall/CRC, 2006

About this Item

Unread book in perfect condition. Seller Inventory # 4199007

Bibliographic Details

Title: Measurement Error in Nonlinear Models : A ...

Publisher: Chapman and Hall/CRC

Publication Date: 2006

Binding: Hardcover

Condition: As New

Edition: 2nd Edition

About this title

Synopsis:

It’s been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and extensively updated to offer the most comprehensive and up-to-date survey of measurement error models currently available.

What’s new in the Second Edition?

·         Greatly expanded discussion and applications of Bayesian computation via Markov Chain Monte Carlo techniques

·         A new chapter on longitudinal data and mixed models

·         A thoroughly revised chapter on nonparametric regression and density estimation

·         A totally new chapter on semiparametric regression

·         Survival analysis expanded into its own separate chapter

·         Completely rewritten chapter on score functions

·         Many more examples and illustrative graphs

·         Unique data sets compiled and made available online

In addition, the authors expanded the background material in Appendix A and integrated the technical material from chapter appendices into a new Appendix B for convenient navigation. Regardless of your field, if you’re looking for the most extensive discussion and review of measurement error models, then Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition is your ideal source.

Review:

This is the second edition of a research-level monograph ... about modeling with predictors that are subject to measurement error ... . The text describes a variety of approaches to handling such data and illustrates the models and methods with numerous examples. The early chapters set the scene with a clear description of the problem through many examples, a discussion of the different types of error, and the distinction between functional and structural models. These two types of models form the basis of the second and third parts ... with the final part devoted to more specialized material including generalized linear structure with an unknown link function, hypothesis testing, and nonparametric regression. ... this edition has been expanded by the inclusion of much more detailed sections, even completely new chapters, on Bayesian MCMC techniques, longitudinal data and mixed models, score functions, and survival analysis. The end result is an up-to-date rigorous treatment of the general ideas and methods of estimation and inference in difficult problems involving nonlinear measurement error models.
-P. Prescott (University of Southampton, UK), Short Book Reviews, December 2006