Abstract
We consider marginal generalized semiparametric partially linear models for clustered data. Lin and Carroll (2001a) derived the semiparametric efficinet score funtion for this problem in the mulitvariate Gaussian case, but they were unable to contruct a semiparametric efficient estimator that actually achieved the semiparametric information bound. We propose such an estimator here and generalize the work to marginal generalized partially liner models. Asymptotic relative efficincies of the estimation or throughout are investigated. The finite sample performance of these estimators is evaluated through simulations and illustrated using a longtiudinal CD4 count data set. Both theoretical and numerical results indicate that properly taking into account the within-subject correlation among the responses can substantially improve efficiency.
Disciplines
Longitudinal Data Analysis and Time Series | Statistical Methodology | Statistical Models | Statistical Theory
Suggested Citation
Wang, Naisyin; Carroll, Raymond J.; and Lin, Xihong, "Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data" (September 2003). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 11.
https://biostats.bepress.com/umichbiostat/paper11
Included in
Longitudinal Data Analysis and Time Series Commons, Statistical Methodology Commons, Statistical Models Commons, Statistical Theory Commons