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.

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Bibliographic Details

Title: Measurement Error in Nonlinear Models
Publisher: Springer
Publication Date: 1995
Language: English
ISBN 10: 0412047217
ISBN 13: 9780412047213
Binding: Hardback
Condition: Very Good

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"I enjoyed reading this book and would recommend it to statisticians for their library." -Journal of Quality Technology ..."This present book is an essential purchase for the library and an excellent purchase for individuals." -International Statistical Institute ..."the book provides an up-to-date introduction to the application of MCMC methods. It is both readable and coherent and should form an essential addition to any modern applied statistician." -The Statistician "It has been a pleasure reading this book...the range of the authors appreciation of the actual issues and challenges pertaining to particular data sets, and extensive theoretical justification of estimation techniques is very impressive." -Biometrics

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