Measurement Error in Nonlinear Models

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

". . . they are to be congratulated for producing a first-rate book that will be an indispensible resource for researchers and other serious students of the statistics of measurement error." – Daniel B. Hall, University of Georgia, in Journal of the American Statistical Association, March 2008, Vol. 103, No. 481“…This book is a successful attempt at collecting, organizing, and presenting the scattered literature in one place. The authors have already successfully accomplished this task in the first edition of this book. The second edition is an extensively revised, enlarged, and improved version of this earlier edition. The updated coverage of techniques and methodologies and the updated bibliography are of great help to those working on the theoretical and applied aspects of the nonlinear measurement error models. This book is a must for all who want to start working in this area. The style of writing and the sequence of topics in the chapters are excellent [and] easily understandable … . Most of the topics are accompanied by illustrated examples, which make understanding of the topics easy. Every chapter concludes with bibliographic notes. Wherever possible, the authors have given an account of the available software … . The derivations of the results are presented separately in an appendix. … This monograph should be of interest and immense help to those interested in the theoretical as well as applied aspects of nonlinear measurement error models. It can also be used as a textbook for a specialized graduate-level course. …” —Statistical Papers, Vol. 49, 2008 ". . . has made an important contribution to the modern perspective of measurement error models. The book would be a valuable addition to any statistical researcher’s library." – Eugenia Stoimenova, Institute of Mathematics and Informatics, in Journal of Applied Statistics, September 2007, Vol. 34, No. 4“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

• Introduction. Important Concepts. Linear Regression and Attenuation. Regression Calibration. Simulation Extrapolation. Instrumental Variables. Score Function Methods. Likelihood and Quasilikelihood. Bayesian Methods. Hypothesis Testing. Longitudinal Data and Mixed Models. Nonparametric Estimation. Semiparametric Regression. Survival Data. Response Variable Error. Appendix A: Background Material. Appendix B: Technical Details. References. Applications and Examples Index. Index.