Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.

This paper considers nonparametric regression to analyze longitudinal binary data. In this paper we propose GEE-Smoothing spline and study the properties of the estimator such as the bias, consistency and efficiency. We use natural cubic spline with combination of generalized estimating equation pro...

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Main Authors: Suliadi, ., Ibrahim, Noor Akma, Daud, Isa, Krishnarajah, Isthrinayagy S.
Format: Article
Language:English
Published: Dixie W Publishing Corporation 2010
Online Access:http://psasir.upm.edu.my/id/eprint/15831/
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author Suliadi, .
Ibrahim, Noor Akma
Daud, Isa
Krishnarajah, Isthrinayagy S.
author_facet Suliadi, .
Ibrahim, Noor Akma
Daud, Isa
Krishnarajah, Isthrinayagy S.
author_sort Suliadi, .
building UPM Institutional Repository
collection Online Access
description This paper considers nonparametric regression to analyze longitudinal binary data. In this paper we propose GEE-Smoothing spline and study the properties of the estimator such as the bias, consistency and efficiency. We use natural cubic spline with combination of generalized estimating equation proposed by Liang & Zeger (1986). We evaluated these properties through simulations and obtained that GEE-Smoothing spline has good properties. The percentage of acceptance of the hypothesis that the function is equal to the true function, using naive and sandwich variance estimators is also obtained. The bias of pointwise estimator is decreasing with increasing sample size. The pointwise estimator is also consistent even using incorrect correlation structure, and the most efficient estimate is obtained if the true correlation structure is used. Example of real data is presented with comparison of GEE with GEE-Smoothing spline.
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publisher Dixie W Publishing Corporation
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spelling upm-158312013-07-25T04:17:48Z http://psasir.upm.edu.my/id/eprint/15831/ Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline. Suliadi, . Ibrahim, Noor Akma Daud, Isa Krishnarajah, Isthrinayagy S. This paper considers nonparametric regression to analyze longitudinal binary data. In this paper we propose GEE-Smoothing spline and study the properties of the estimator such as the bias, consistency and efficiency. We use natural cubic spline with combination of generalized estimating equation proposed by Liang & Zeger (1986). We evaluated these properties through simulations and obtained that GEE-Smoothing spline has good properties. The percentage of acceptance of the hypothesis that the function is equal to the true function, using naive and sandwich variance estimators is also obtained. The bias of pointwise estimator is decreasing with increasing sample size. The pointwise estimator is also consistent even using incorrect correlation structure, and the most efficient estimate is obtained if the true correlation structure is used. Example of real data is presented with comparison of GEE with GEE-Smoothing spline. Dixie W Publishing Corporation 2010 Article PeerReviewed Suliadi, . and Ibrahim, Noor Akma and Daud, Isa and Krishnarajah, Isthrinayagy S. (2010) Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline. Journal of Applied Probability and Statistics, 5 (1). pp. 77-93. ISSN 1930-6792 English
spellingShingle Suliadi, .
Ibrahim, Noor Akma
Daud, Isa
Krishnarajah, Isthrinayagy S.
Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.
title Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.
title_full Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.
title_fullStr Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.
title_full_unstemmed Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.
title_short Nonparametric regression for longitudinal binary data based on GEE-Smoothing Spline.
title_sort nonparametric regression for longitudinal binary data based on gee-smoothing spline.
url http://psasir.upm.edu.my/id/eprint/15831/