Measurement Error in Nonlinear Models (Monographs on Statistics and Applied Probability)

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.

"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