| 主讲人 |
林华珍 教授 |
简介 |
<div><b>Abstract</b><b>：</b>In this paper we develop a new semi-parametric regression model for longitudinal data. In the new model, we allow the transformation function and the baseline function to be unknown. The proposed model can provide a much broader class of models than the existing additive and multiple models. Our estimators for the regression parameters, the transformation function and the baseline function are asymptotically normal, particularly, the estimators for regression parameters and the transformation function converge to their true values at the rate $n^{-1/2}$, the convergence rate that one could expect for a parametric model. In a simulation study, we demonstrate that the proposed semiparametric method is robust with little loss of efficiency. Finally, we apply the new method to a study on longitudinal health care costs.</div>
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<div><b>Key words:</b> Semiparametric; transformation model; additive model; multiple model; longitudinal data.</div>
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<div>This is a co-working paper with Ling Zhou and Xiao-Hua Zhou.</div>
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