Description
Book SynopsisIncorporates mixed-effects modeling techniques for more powerful and efficient methods
This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented.
With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques.<
Trade Review"The authors should be congratulated for their contribution…a nice addition to the personal collection of any statistician." (
Journal of the American Statistical Association, June 2007)
"...can serve as a textbook for both undergraduate and graduate students. Also it will help researchers in this area…[because of its] comprehensive coverage of the materials." (Mathematical Reviews, 2007b)
"…an excellent survey of many of the nonparametric regression techniques used in longitudinal studies…highly recommended." (CHOICE, October 2006)
Table of ContentsPreface.
Acronyms.
1. Introduction.
2. Parametric Mixed-Effects Models.
3. Nonparametric Regression Smoothers.
4. Local Polynomial Methods.
5. Regression Spline Methods.
6. Smoothing Splines Methods.
7. Penalized Spline Methods.
8. Semiparametric Models.
9. Time-Varying Coefficient Models.
10. Discrete Longitudinal Data.
References.
Index.
