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学术报告—Checking the Adequacy of Functional Linear Quantile Regression Model

2019-09-04   审核人:

报 告 人:杜江 副教授

主 持 人:张晓颖

时    间:2019年9月6日 16:00-17:00

地    点:第三教学楼五楼大数据实验室

主办单位:理学院

报告人简介:杜江,副教授,统计学博士,硕士生导师。2016年入选北京工业大学日新人才计划;2019年入选北京市教委青年拔尖人才计划。现为美国数学评论评论员、北京应用统计协会理事、中国青年统计学家协会理事、中国工业与应用数学学会会员、河北省研究生数学建模评审专家。主持国家自然科学基金面上项目和青年项目各1项、中国博士后基金(面上)1项、北京市教委科技计划项目1项;参加国家重点研发计划1项、国家自然科学基金5项、国家社科科学基金1项、省部级项目3项和多项横向项目。已在国内外著名学术刊物上发表论文30余篇,其中20余篇被SCI检索。

观点综述:In this talk, we present a novel model checking method for functional linear quantile regression model (FLQRM). FLQRMis widely used to characterize the relationship between a scalar response and a functional covariate. Most existing research results are based on a correct assumption that the response is related to the functional predictor through a linear model for given quantile level. Thistalkfocuses on investigating the adequacy check of the functional linear quantile regression model. We propose a nonparametric kernel-based test statistic by using the functional principal component analysis. It is proved that the test statistic follows a normal distribution asymptotically under the null hypothesis and diverges to infinity for any misspecified models. Therefore, the test is consistent against any fixed alternative. Moreover, it is shown that the test has asymptotic power one for the local alternative hypothetical models converging to the null hypothesis. The finite sample properties of the test statistic are illustrated through extensive simulation studies. A real data set of 24 hourly measurements of ozone levels in Sacramento, California is analyzed by the proposed test.

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