Measurement Error in Nonlinear Models [Hardcover]

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

Book Description

Publication Date: July 6, 1995 | ISBN-10: 0412047217 | ISBN-13: 978-0412047213 | Edition: 1

This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.

Product Details

• Hardcover: 312 pages
• Publisher: Chapman and Hall/CRC; 1 edition (July 6, 1995)
• Language: English
• ISBN-10: 0412047217
• ISBN-13: 978-0412047213
• Product Dimensions: 9.4 x 6 x 0.9 inches
• Shipping Weight: 1.3 pounds
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #3,719,890 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

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.