报告题目：Statistical inference in partial functional linear expectile regression model

报告时间：2021年12月16日下午20:00

会议链接：https://meeting.tencent.com/dm/ckp4Kj9JU45h

会议 ID：782-197-524

主办单位：科研处/数学与统计学院

主讲人：余平

余平简介：博士毕业于北京工业大学统计学专业，复旦大学和香港中文大学博士后。山西师范大学硕士生导师，全国工业统计学教学研究会理事,中国青年统计学家协会理事。

研究方向：函数型数据分析、稳健统计、分位数回归等。近5年在国内外学术刊物“中国科学.数学”中英文版,“Computational Statistics & Data Analysis”，“Metrika”、“Statistical Papers”、 Computational Statistics等上发表学术论文18余篇，14篇被SCI检索。

报告摘要：As extensions of mean, expectiles embrace all the distribution information of a random variable. Expectile regression is computationally friendlier because the asymmetric least squares loss function is differentiable everywhere. This regression also enables effective estimation of the expectiles of a response variable when potential explanatory variables are given. In this study, we propose the partial functional linear expectile regression  model. The slope function and constant coefficients are estimated by using the functional principal component basis. The convergence rate of the slope function and the asymptotic normality of the parameter vector are established. To inspect the effect of the parametric component on the response variable, we develop Wald-type and expectile rank score tests and establish their asymptotic properties. The finite performance of the proposed estimators and test statistics are evaluated through simulation study. Results indicate that the proposed estimators are comparable to competing estimation methods and the newly proposed expectile rank score test is useful. The methodologies are illustrated by using two real data examples.