Measurement Error in Nonlinear Models

<p>It’s been over a decade since the first edition of <i>Measurement Error in Nonlinear Models</i> 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 <b>Measurement Error in Nonlinear Models: A Modern Perspective Second Edition</b> has been revamped and extensively updated to offer the most comprehensive and up-to-date survey of measurement error models currently available.</p><p><i>What’s new in the Second Edition?</i> </p><p>· <b>Greatly expanded</b> discussion and applications of Bayesian computation via Markov Chain Monte Carlo techniques </p><p>· A <b>new chapter</b> on longitudinal data and mixed models </p><p>· A<b> thoroughly revised</b> chapter on nonparametric regression and density estimation </p><p>· A <b>totally new</b> chapter on semiparametric regression </p><p>· Survival analysis <b>expanded</b> into its own separate chapter </p><p>· <b>Completely rewritten</b> chapter on score functions </p><p>· <b>Many more</b> examples and illustrative graphs </p><p>· <b>Unique data sets</b> compiled and made available online </p><p>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 <b>Measurement Error in Nonlinear Models: A Modern Perspective Second Edition</b> is your ideal source.</p>