Nonparametric Regression Methods for Longitudinal Data Analysis

Incorporates mixed-effects modeling techniques for more powerfuland efficient methods This book presents current and effective nonparametric regressiontechniques for longitudinal data analysis and systematicallyinvestigates the incorporation of mixed-effects modeling techniquesinto various nonparametric regression models. The authors emphasizemodeling ideas and inference methodologies, although sometheoretical results for the justification of the proposed methodsare presented. With its logical structure and organization, beginning with basicprinciples, the text develops the foundation needed to masteradvanced principles and applications. Following a brief overview,data examples from biomedical research studies are presented andpoint to the need for nonparametric regression analysis approaches.Next, the authors review mixed-effects models and nonparametricregression models, which are the two key building blocks of theproposed modeling techniques. The core section of the book consists of four chapters dedicated tothe major nonparametric regression methods: local polynomial,regression spline, smoothing spline, and penalized spline. The nexttwo chapters extend these modeling techniques to semiparametric andtime varying coefficient models for longitudinal data analysis. Thefinal chapter examines discrete longitudinal data modeling andanalysis. Each chapter concludes with a summary that highlights key pointsand also provides bibliographic notes that point to additionalsources for further study. Examples of data analysis frombiomedical research are used to illustrate the methodologiescontained throughout the book. Technical proofs are presented inseparate appendices. With its focus on solving problems, this is an excellent textbookfor upper-level undergraduate and graduate courses in longitudinaldata analysis. It is also recommended as a reference forbiostatisticians and other theoretical and applied researchstatisticians with an interest in longitudinal data analysis. Notonly do readers gain an understanding of the principles of variousnonparametric regression methods, but they also gain a practicalunderstanding of how to use the methods to tackle real-worldproblems.