Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition [Hardcover]

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

Book Description

ISBN-10: 1584886331 | ISBN-13: 978-1584886334 | Publication Date: June 21, 2006 | Edition: 2

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.

Product Details

• Hardcover: 488 pages
• Publisher: Chapman and Hall/CRC; 2 edition (June 21, 2006)
• Language: English
• ISBN-10: 1584886331
• ISBN-13: 978-1584886334
• Product Dimensions: 9.1 x 6.3 x 1.3 inches
• Shipping Weight: 1.4 pounds (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #296,407 in Books (See Top 100 in Books)

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Most Helpful Customer Reviews

31 of 31 people found the following review helpful:

5.0 out of 5 stars another difficult topic in regression analysis tackled by Ray Carroll, February 20, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Measurement Error in Nonlinear Models (Hardcover)

Ray Carroll and David Ruppert are well known research statisticians who have published many joint articles on regression, weighted regression and transformation and they have also written an excellent book together on this research topic. Stefanski has recently published several papers on measurement error models with Carroll. Here they have teamed up to write a statistics text on a unique topic. Measurement error models are common and practical when dealing with covariates that have measurement error. Least squares estimation in linear regression is based on the assumption that the predictor variables are measured without error. There are many articles and an excellent text by Fuller "Measurement Error Models", published by Wiley in 1988 that deals with the linear case. Also look at a section in Chapter 5 of Miller's "Beyond ANOVA, Basics of Applied Statistics" that refers to the problem as the error in variables problem. For the nonlinear case this is the first treatment. Well written and well documented, this text provides an up-to-date account of the theory and methods and provides real applications (e.g. the Framingham Heart Study). This is a great reference as are many of the other monographs in this series by Chapman and Hall/CRC Press. Includes bootstrap approaches in the chapter on fitting methods and models.

1 of 2 people found the following review helpful:

5.0 out of 5 stars very good book about measurement Error, September 5, 2008

By 

J. Gan "kevin" (Pheladephina, PA.USA) - See all my reviews
(REAL NAME)   

This review is from: Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition (Hardcover)

I am doing my BIostatistics dissertation about Measurement Error for Survey data, I thought you textbook is one of the classical books I have ever used. I bought this book from Amazion, which is the lowest price compared to other places, and I received my book very quick and safety. That is good.