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.
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