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.
Inhaltsverzeichnis
Preface.
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.
Über den Autor
HULIN WU, PHD, is Professor of Biostatistics in the School
of Medicine and Dentistry at the University of Rochester in the
Departments of Medicine; Community and Preventative Medicine; and
Biostatistics and Computational Biology. His research interests
include longi-tudinal data, HIV/AIDS modeling, biomedical
informatics, and clinical trials.
JIN-TING ZHANG, PHD, is Assistant Professor in the
Department of Statistics and Applied Probability at the National
University of Singapore. His research interests include
nonparametric regression and density estimation, nonparametric
mixed-effects modeling, functional data analysis, and longitudinal
data analysis, among others.
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