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Semiparametric regression analysis of longitudinal skewed data

作者: 发布时间:2014-11-10 点击数:
主讲人:林华珍 教授
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林华珍教授简历

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AbstractIn 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.
 
Key words: Semiparametric;  transformation  model; additive model; multiple model; longitudinal data.
 
 
This is a co-working paper with Ling Zhou and Xiao-Hua Zhou.
 
时间:2014-11-10 (Mon) 16:30-18:00
地点:N303 经济楼/Economics Building
讲座语言:中文
主办单位:王亚南经济研究院、永利集团3044官网欢迎您
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期数:永利集团3044官网欢迎您高级计量经济学与统计学系列讲座2014秋季学期第四讲(总第47讲)
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