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Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition de Raymond J. Carroll

Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Autor Raymond J. Carroll, David Ruppert, Leonard A. Stefanski, Ciprian M. Crainiceanu
en Limba Engleză Hardback – 21 iun 2006
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.
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Specificații

ISBN-13: 9781584886334
ISBN-10: 1584886331
Pagini: 484
Ilustrații: 75 b/w images
Dimensiuni: 152 x 229 x 31 mm
Greutate: 1.06 kg
Ediția:2Revizuită
Editura: CRC Press
Colecția Chapman and Hall/CRC
Seria Chapman & Hall/CRC Monographs on Statistics and Applied Probability

Public țintă

Professional and Professional Practice & Development

Cuprins

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.

Recenzii

". . . 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

Notă biografică

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

Descriere

Written by leading authorities in nonlinear regression modeling, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition provides an up-to-date overview of analysis strategies for regression models in which variables are measured with errors. Thoroughly revised, this second edition includes additional material on Bayesian methods and semiparametric regression and a new chapter on generalized linear mixed models. Focusing on general ideas and strategies of estimation and inference, the authors provide various detailed worked examples, computed using MATLAB and R, from the fields of epidemiology and biometry. Data sets and software for all of the examples are available for download from the Internet.