主讲：郭旭
时间：2017年11月1日 9:00-10:00
地点：明德主楼 1016会议室
摘要：
Non-parametric generalized likelihood ratio test is a popular method of model checking for regressions. However, there are two issues that may be the barriers for its powerfulness: existing bias term and curse of dimensionality. The purpose of this paper is thus twofold: a bias reduction is suggested and a dimension reduction-based adaptive-to-model enhancement is recommended to promote the power performance. The proposed test statistic still possesses the Wilks phenomenon and behaves like a test with only one covariate. Thus, it converges to its limit at a much faster rate and is much more sensitive to alternative models than the classical non-parametric generalized likelihood ratio test. As a by-product, we also prove that the bias-corrected test is more efficient than the one without bias reduction in the sense that its asymptotic variance is smaller. Simulation studies and a real data analysis are conducted to evaluate of proposed tests.
简介：
郭旭，男，1988年生。2014年于香港浸会大学获统计学博士学位，现任北京师范大学统计学院副教授，硕士生导师。研究方向为模型检验、高维数据分析、不确定下的行为决策、缺失数据分析等。现担任ESCI收录期刊Annals of Financial Economics(http://www.worldscientific.com/worldscinet/afe)和EconLit收录期刊Theoretical Economics Letters (http://www.scirp.org/journal/TEL/)的副主编，美国《Mathematical Review》评论员，多个统计学和经济学期刊的审稿人。在统计学顶级期刊Journal of the Royal Statistical Society: Series B和 Biometrika，统计学主流期刊Statistics and Computing, Journal of Multivariate Analysis, Computational Statistics & Data Analysis和经济学主流期刊 Insurance: Mathematics and Economics, North American Journal of Economics and Finance, Economics Letters, 和Economic Modelling等SCI和SSCI期刊发表论文近30篇。