【统计大讲堂】郭旭:非参数广义似然比检验的改进——偏差校正与降维
(统计学院发布于:2017-10-27 18:03:20)

题目:Enhancements of Non-parametric Generalized Likelihood Ratio Test: Bias Correction and Dimension Reduction

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.

个人简介:郭旭,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和EconLit收录期刊Theoretical Economics Letters 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。

讲座时间:11月1日(周三)9:00-10:00

讲座地点:明德主楼1016会议室

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