Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed-Effects Modeling ApproachesJohn Wiley & Sons, 12 трав. 2006 р. - 400 стор. Incorporates 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. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems. |
Інші видання - Показати все
Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed ... Hulin Wu,Jin-Ting Zhang Попередній перегляд недоступний - 2006 |
Nonparametric Regression Methods for Longitudinal Data Analysis: Mixed ... Hulin Wu,Jin-Ting Zhang Попередній перегляд недоступний - 2006 |
Загальні терміни та фрази
ACTG 388 data approximate asymptotic backfitting algorithm bandwidth basis functions Bayesian Chapter coefficient coefficient functions computed covariance function covariance matrix cross-validation defined definition denote design matrix design time points Diggle distinct design EM-algorithm Figure first fitted fitting fixed GCV rule i-th subject individual curves Lin and Carroll linear model linear smoother LME model log-likelihood longitudinal data longitudinal data analysis LPK method LPK-GEE LPME maximum likelihood MEPS MERS fit MESS model minimizers mixed-effects models model complexity NLME nonparametric regression NPME NPS fits NRS fit number of knots obtained P-spline parametric model PLS criterion pointwise SD band polynomial population mean function progesterone data proposed roughness matrix S-PLUS Section selected semiparametric models smoother matrix smoothing parameter smoothing spline method SNLME specified SPME model standard subject-specific techniques Theorem truncated power basis TVC-NPM model variance components Wang within-subject correlation Wu and Zhang
Посилання на книгу
Correlated Data Analysis: Modeling, Analytics, and Applications Peter X. -K. Song Обмежений попередній перегляд - 2007 |