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[10月25日]计量、金融和大数据分析workshop

发布日期：2019-10-23 09:47    来源：

【题目】： Neyman's Smooth Tests for Nonparametric Models

【主讲人】：宋晓军   Assistant Professor, Peking University

【时间】：2019年10月25日（周五）10：00-11：30

【地点】：北大经济学院107室

【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, various smooth tests have been proposed to assess the goodness-of-fit of certain parametric distributions. Nevertheless, the majority of the existing literature focuses on PIT in parametric models, even although Neyman (1937)'s idea is 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

Assistant Professor, Guanghua School of Management, Peking University

PhD in Economics, 2014, Universidad Carlos III de Madrid

Fields:计量经济学;非参数与半参数方法;模型设定;检验自助方法;时间序列方法


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