【4月21日】统计学学术讲座

报告题目：Neyman's Smooth Tests for Nonparametric Models（非参数模型的Neyman光滑检验）

主讲人：宋晓军副教授（北京大学）

时间：2023年4月21日（周五）10:00 a.m.

地点：北院卓远楼305会议室

主办单位：统计与数学学院

摘要：Neyman (1937)'s smooth test has proven to be an extremely valuable tool in the long history of statistical hypothesis testing. Smooth tests are inspired from the probability integral transform (PIT); for example, smooth tests have been proposed to assess the goodness-of-fit of various popular parametric distributions. Nevertheless, the majority of the exisiting literature focuses on PIT in parametric models, even though Neyman (1937)'s idea is general and easily applicable to PIT constructed from nonparametric models. In this talk I mainly discuss the promising aspects of the smooth tests for nonparametric models. In particular, I focus on smooth tests for (i) conditional independence, (ii) copula independence, and (iii) the equality of (conditional) distributions as well as the equality of copulas in the two-sample settings. 

主讲人简介：

宋晓军，男，北京大学光华管理学院商务统计与经济计量系副教授，博士生导师，西班牙马德里卡洛斯三世大学经济学博士。主要研究兴趣是理论计量经济学，包括非参数/半参数方法，假设检验和自助法，以及计量经济学的应用等。论文发表在Econometric Theory，Journal of Applied Econometrics，Journal of Business & Economic Statistics和Journal of Econometrics等国际期刊。主持和参加自然科学基金面上项目和国家重点专项等。目前担任Economic Modelling副主编。