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Abstract:
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In this paper, we consider the estimation of both the parameters and the nonparametric link function in partially linear single-index models for unbalanced longitudinal data. In par- ticular, a new three-stage approach is proposed to estimate the nonparametric link function using the marginal kernel regression and the parametric component with generalized estimat- ing equations. Both estimates account for the within-subject correlation. We show that the estimation of the parameters are asymptotically semiparametric efficient. We also show the estimation of the link function is more efficient than the method by Chen et al. (2015). The finite sample performance of the proposed method is demonstrated by simulation studies. In addition, two real data examples are analyzed to illustrate the methodology.
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