Measurement Error in Nonlinear Models

Written by leading authorities in nonlinear regression modeling <b>Measurement Error in Nonlinear Models: A Modern Perspective Second Edition</b> 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.