Sieve Estimation and Variable Selection in Sparse Semiparametric Single Index Models

董朝华

<p>The talk concerns sieve estimation in sparse semiparametric single index models. The use of Hermite polynomial in approximating the unknown link function provides a convenient framework to conduct both estimation and variable selection. The estimation of the index parameter is formulated from solutions which are obtained by the routine penalized weighted linear regression procedure, where the weights are used in order to tackle the unbounded support of the regressors. The resulting index parameter estimator is shown to be consistent and sparse. The asymptotic normality of the estimator of the index and that of the link function are established. Numerical results show that both the variable selection procedure and the associated estimators perform well in finite samples.</p>

2018-12-03(Monday)16:40-18:00

D235

中文

统计系

统计系

系列讲座

陈力

<p><xformflag flagtype="xform_textarea" flagid="fd_360a4f3b37da04"></xformflag>董朝华 中南财经政法大学 教授<br /> 个人信息:<br /> 经济学博士，Journal of time series analysis，Journal of econometrics, Journal of nonparametric statistics, Journal of testing and evaluation and African journal of business management 等期刊的匿名审稿人。<br /> 教育背景:<br /> 博士后研究员：澳大利亚莫纳什大学(Monash University)，2015<br /> 经济学博士： 澳大利亚阿德莱德大学(the University of Adelaide)，2012<br /> 研究兴趣：<br /> 时间序列模型，面板数据模型，微观计量和金融计量，非参数和半参数方法.</p>

高级经济学与统计学系列讲座2018秋季学期第六讲（总第110讲）