题目：
Generalized Additive Partial Linear Models With High-dimensional Covariates
时间：
2011.07.26 15:00—16:00
地点：
创新园大厦
A1101
报告人简介：
梁华，美国罗切斯特大学生物统计学教授。
1992
年于中科院获博士学位，
2001年于美国德州农工大学
获博士学位，目前担任美国罗切斯特大学医学中心生物统计与计算生物学系教授，
JASA, Journal of Nonparametric Statistics, Journal of Systems Science and Complexity, Biostatistics
副主编
, Elected member of the International Statistical Institute,
Fellow of the Royal Statistical Society,
1996- 1998
年德国洪堡大学洪堡学者（
Alexander von Humboldt Research Fellow)
。梁华教授在半参数统计推断尤其是部分线性模型方面取得了一系列在国际上有影响的工作。
摘要：
We study generalized additive partial linear models when the dimensions of both nonlinear and linear covariates diverge with the sample size. We are interested in which components (including parametric and nonparametric ones) are non-zero. The additive nonparametric functions are approximated by polynomial splines. We propose a doubly penalized procedure to obtain an initial estimate and then use the adaptive LASSO to identify non-zero components and to obtain the final selection and estimation results. We establish selection and estimation consistency of the estimator as well as asymptotic normality for the estimator of the parametric components by employing a penalized quasi-likelihood. Thus our estimator is shown to have an asymptotic oracle property. Monte Carlo simulations show the proposed procedure works well with moderate sample sizes. An empirical example is examined to illustrate the application of the method.
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本次报告负责人：王晓光 老师
主办单位：bg视讯
