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Wednesday, May 20, 2020 | History

8 edition of Nonparametric regression methods for longitudinal data analysis found in the catalog.

Nonparametric regression methods for longitudinal data analysis

mixed-effects modeling approaches

by Hulin Wu

  • 386 Want to read
  • 16 Currently reading

Published by Wiley-Interscience in New York .
Written in English

    Subjects:
  • Nonparametric statistics.,
  • Longitudinal method -- Mathematical models.

  • Edition Notes

    Includes bibliographical references and index.

    StatementHulin Wu, Jin-Ting Zhang.
    ContributionsZhang, Jin-Ting, 1964-
    Classifications
    LC ClassificationsQA278.8 .W8 2006
    The Physical Object
    Paginationp. cm.
    ID Numbers
    Open LibraryOL3432184M
    ISBN 100471483508
    LC Control Number2005058356

    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. also be applied to construct nonparametric regression estimates, where one does not restrict the class of possible relationships, and will be one of the approaches studied in this book. Linear regression analysis, based on the concept of a regression function, was introduced by F. Galton in , while a probabilistic approach in the.

    Abstract. In Chapter 2, I review a number of classical methods traditionally applied in longitudinal data analysis. First, several descriptive approaches are delineated, including time plots of trend, the paired t-tests, and effect sizes and their confidence -analysis is also described, with the remaining issues in this technique being discussed. Smoothing Parameter Nonparametric Regression Kernel Estimate Kernel Estimator Smoothing Spline These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm by: 1.

    Nonparametric regression is a methodology for describing the trend between a response variable and one or more predictors. This approach differs from classical regression models in that it does not rely on strong assumptions regarding the shape of the relationship between the variables. Comparison of parametric and nonparametric methods for outcome prediction using longitudinal data after cardiac arrest K-means is an unsupervised learning method commonly used to identify clusters of individuals within data. Longitudinal extensions of the method have been compared with GBTM for Conceptualization, methodology, data.


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Nonparametric regression methods for longitudinal data analysis by Hulin Wu Download PDF EPUB FB2

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.

Very good introductory text for nonparametric regression. This book first gives motivation for the processes, outlines the methods of smoothing, and then covers various regression techniques. Not particularly mathematical, although some linear algebra knowledge is necessary.

Very accessible as a 4/5(1). 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. Incorporates mixed-effects modeling techniques for more powerful and efficient methodsThis 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.

Nonparametric Regression Analysis of Longitudinal Data (Lecture Notes in Statistics) Softcover reprint of the original 1st ed. Edition and also to provide intuition about the methods and applications. Applications to the analysis of longitudinal studies are emphasized to encourage the non-specialist and applied statistician to try Cited by: 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.

Online retailer of specialist medical books, we also stock books focusing on veterinary medicine. Order your resources today from Wisepress, your medical bookshop. This monograph reviews some of the work that has been done for longitudi­ nal data in the rapidly expanding field of nonparametric regression.

The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods. subjecthetween-subject variations of longitudinal data. The purpose of this book is to survey the nonparametric regression techniques for longitudinal data analysis which are widely scattered throughout the literature, and more importantly, to sys- tematically investigate the incorporation of mixed-effects modeling techniques into.

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision : Colin O.

Wu, Xin Tian. (). Nonparametric Regression Analysis of Longitudinal Data. Journal of the American Statistical Association: Vol. 93, No.pp.

Cited by: This monograph reviews some of the work that has been done for longitudi­ nal data in the rapidly expanding field of nonparametric regression.

The aim is to give the reader an impression of the basic mathematical tools that have been applied, and also to provide intuition about the methods Brand: Springer-Verlag New York. Nonparametric approaches have recently emerged as a flexible way to model longitudinal data. This entry reviews some of the common nonparametric approaches to incorporate time and other covariate.

Nonparametric Regression Analysis of Longitudinal Data - Ebook written by Hans-Georg Müller. Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Nonparametric Regression Analysis of Longitudinal Data.1/5(1).

Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis.

This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques.

The authoritative reference on nonparametric methods for evaluating longitudinal data in factorial designs Broadening the range of techniques that can be used to evaluate longitudinal data, Nonparametric Analysis of Longitudinal Data in Factorial Experiments presents nonparametric methods of evaluation that supplement the generalized linear.

Other longitudinal data books do not have the breadth of this one. I highly recommend this book to anyone interested in learning about modern methods for longitudinal data analysis. I think it would make a particularly good book for a Ph.D.-level reading course or as a supplement to a longitudinal data textbook in a graduate-level course.

Get this from a library. Nonparametric regression methods for longitudinal data analysis: [mixed-effects modeling approaches]. [Hulin Wu; Jin-Ting Zhang] -- "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.

For a comprehensive introduction to GPs we refer the reader to the book 5 and the Methods Regression Methods for Longitudinal Data non-parametric analysis of longitudinal by: 6. over kernel methods in nonparametric regression models with longitudinal data.

Lin and Carroll () considered a wide class of semiparametric problems for the mean structure. Compared to the models for the mean in longitudinal data analysis, model based analysis for the covariance is much less studied. To address this issue, we propose.tive to handle longitudinal data.

For readers searching for such nonparametric approaches in the literature, a keyword to include is ”functional data” in addition to ”longitudinal data”. The two books [20] and [21] on functional data analysis provide an excellent introduction to 2. T1 - Nonparametric Regression Analysis of Longitudinal Data.

AU - Staniswalis, Joan G. AU - Lee, Jiun-Kae Jack. PY - /12/1. Y1 - /12/1. N2 - Nonparametric methods are developed for estimating the dose effect when a response consists of correlated observations over time measured in a dose–response by: