Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition

Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition

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><i>What’s new in the Second Edition?</i> <p>·         <b>Greatly expanded</b> discussion and applications of Bayesian computation via Markov Chain Monte Carlo techniques <p>·         A <b>new chapter</b> on longitudinal data and mixed models <p>·         A<b> thoroughly revised</b> chapter on nonparametric regression and density estimation <p>·         A <b>totally new</b> chapter on semiparametric regression <p>·         Survival analysis <b>expanded</b> into its own separate chapter <p>·         <b>Completely rewritten</b> chapter on score functions <p>·         <b>Many more</b> examples and illustrative graphs <p>·         <b>Unique data sets</b> compiled and made available online <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.