We consider the marginal longitudinal model in which repeated measuresSemiparametric Efficiency in Longitudinal Marginal Models

Raymond J. CarrollTexas A&M University

are taken on each individual. The model is in partially linear form m(X)

+ Zb up to a link function, and interest is in estimating the regression

parameter b. Profile kernel methods are developed to estimate b: these

methods have the feature that each smoothing step can be performed with

bandwidths that are of the usual rate. The methods are shown to be

semiarametric efficient in the Gaussian model. We contrast our methods

with the standard device of working independence, and show that

considerable gains in efficiency are possible. The same results are

shown to hold in the Gaussian case when smoothing splines are the

nonparametric driver: this requires that we derive the bias and variance

expressions for smoothing splines in independent and correlated data.