Longitudinal data analysis using nonparametric regression model

Ibrahim, Noor Akma and Suliadi, (2009) Longitudinal data analysis using nonparametric regression model. In: ICCS-X, 20-23 December 2009, Cairo, Egypt.

[img] Microsoft Word
Restricted to Repository staff only



This paper proposes nonparametric regression model to analyze longitudinal data. We combine natural cubic spline with generalized estimating equations (GEE) to handle unknown function of the covariate and accounting for the correlation within subjects. Specific condition in which we assume independence, AR-1 and exchangeable correlation structures from each subject with varying sample size are used in the simulation study to assess the efficiency of the estimators. A real data application of the proposed model is illustrated with comparison to parametric model and GEE-smoothing spline under independence assumption.

Item Type:Conference or Workshop Item (Paper)
Keyword:longitudinal data, nonparametric regression, correlation, generalized estimating equations, smoothing spline
Subject:Longitudinal method
Subject:Regression analysis
Subject:Correlation (Statistics)
Faculty or Institute:Institute for Mathematical Research
ID Code:11363
Deposited By: Samsida Samsudin
Deposited On:31 Mar 2011 04:09
Last Modified:08 Nov 2013 07:50

Repository Staff Only: Edit item detail

Document Download Statistics

This item has been downloaded for since 31 Mar 2011 04:09.

View statistics for "Longitudinal data analysis using nonparametric regression model"

Universiti Putra Malaysia Institutional Repository

Universiti Putra Malaysia Institutional Repository is an on-line digital archive that serves as a central collection and storage of scientific information and research at the Universiti Putra Malaysia.

Currently, the collections deposited in the IR consists of Master and PhD theses, Master and PhD Project Report, Journal Articles, Journal Bulletins, Conference Papers, UPM News, Newspaper Cuttings, Patents and Inaugural Lectures.

As the policy of the university does not permit users to view thesis in full text, access is only given to the first 24 pages only.