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http://dx.doi.org/10.7465/jkdi.2014.25.1.211

Upgraded quadratic inference functions for longitudinal data with type II time-dependent covariates  

Cho, Gyo-Young (Department of Statistics, Kyungpook National University)
Dashnyam, Oyunchimeg (Department of Mathematics and Statistics, Mongolian State University of Education)
Publication Information
Journal of the Korean Data and Information Science Society / v.25, no.1, 2014 , pp. 211-218 More about this Journal
Abstract
Qu et. al. (2000) proposed the quadratic inference functions (QIF) method to marginal model analysis of longitudinal data to improve the generalized estimating equations (GEE). It yields a substantial improvement in efficiency for the estimators of regression parameters when the working correlation is misspecified. But for the longitudinal data with time-dependent covariates, when the implicit full covariates conditional mean (FCCM) assumption is violated, the QIF can not provide more consistent and efficient estimator than GEE (Cho and Dashnyam, 2013). Lai and Small (2007) divided time-dependent covariates into three types and proposed generalized method of moment (GMM) for longitudinal data with time-dependent covariates. They showed that their GMM type II and GMM moment selection methods can be more ecient than GEE with independence working correlation (GEE-ind) in the case of type II time-dependent covariates. We develop upgraded QIF method for type II time-dependent covariates. We show that this upgraded QIF method can provide substantial gains in efficiency over QIF and GEE-ind in the case of type II time-dependent covariates.
Keywords
FCCM assumption; GEE; GMM; longitudinal data; marginal model; QIF; time-dependent covariates;
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Times Cited By KSCI : 2  (Citation Analysis)
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