UPM Institutional Repository

GEE-smoothing spline for semiparametric estimation of longitudinal binary data


Suliadi, and Ibrahim, Noor Akma and Daud, Isa and Krishnarajah, Isthrinayagy S. (2010) GEE-smoothing spline for semiparametric estimation of longitudinal binary data. International Journal of Applied Mathematics and Statistics, 18 (S10). pp. 82-95. ISSN 0973-1377; ESSN: 0973-7545


This paper considers analyzing longitudinal data semiparametrically and proposing GEE-Smoothing spline in the estimation of the parametric and nonparametric components. Generalized estimating equation is used as the core of the estimation. Estimation of association or within subject correlation used method of moment suggested by Liang and Zeger (1986). In the estimation of nonparametric component, we used smoothing spline approach specifically the natural cubic spline. We show through simulation that GEE-Smoothing Spline has good properties. The bias of parametric and nonparametric estimators decrease with increasing sample size. These estimators are also consistent even though incorrect correlation structure is used. The most efficient estimator can be obtained if the correct correlation structure is used rather than ignore the dependency.

Download File

Full text not available from this repository.

Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Publisher: Centre for Environment, Social and Economic Research Publications
Keywords: Longitudinal binary data; Semiparametric estimation; Generalized estimating equation; Natural cubic spline; Consistency; Efficiency
Depositing User: Nurul Ainie Mokhtar
Date Deposited: 31 Jul 2015 06:49
Last Modified: 31 Jul 2015 06:49
URI: http://psasir.upm.edu.my/id/eprint/14854
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item