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.

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.