题目：Nonparametric Regression with Covariates Subject to Dependent Censoring
报告人：黄磊 西南交通大学
时间：2020.11.29 08：40-09：40
腾讯会议: ：112 255 466
报告摘要：
Censoring occurs often in data collection. In this paper, we consider nonparametric regression when the covariate is censored under general settings. In contrast to censoring in the response variable in survival analysis, regression with censored covariates is more challenging. We propose to estimate the regression function using conditional hazard rates. Asymptotic normality of our proposed estimator is established. Both theoretical results and simulation studies demonstrate that the proposed method is more efficient than that based on complete observations and other methods, especially when the censoring rate is high. We illustrate the usefulness of the method using a well-known dataset from a randomized placebo controlled clinical trial of the drug D-penicillamine.
报告人简介：
黄磊，2015年博士毕业于新加坡国立大学，现任职于西南交通大学数学学院统计系，副教授，硕士研究生导师，主要研究方向有半参数时间序列模型、金融统计分析、医学生物统计。已发表SCI期刊文章10多篇，其中若干篇发表在Annals of Statistics, Statistical Methods in Medical Research, Statistics in Medicine, Computational Statistics & Data Analysis, Journal of Statistical Computation and Simulation等期刊上。主持自然科学基金青年项目一项，参与自然科学基金面上项目、青年项目各一项，2017年留基委(CSC)访问学者。
题目：Median of means approach for Pearson's correlations and High-dimensional Compositional Data
报告人：：刘鹏飞 江苏师范大学
时间：2020.11.29 09：50-10：50
腾讯会议: ：112 255 466
报告摘要：
We propose Median of Means type nonparametric estimator for Pearson’s correlation coefficient which has been used widely in various disciplines. Under certain condition on the growing rate of the number of subgroups, the consistency and asymptotic normality of proposed estimator are investigated. Furthermore, we construct a new method to test Pearson correlation coefficient based on the empirical likelihood method for median. Extensively numerical simulations are designed to demonstrate the superiorities of our estimator. It is shown that the new proposed estimator is quite robust with respect to outliers. We also address the challenges of covariance estimation for high-dimensional Compositional data. Assuming the basis covariance matrix lying in a well-recognized class of sparse covariance matrices, we adopt a proxy matrix known as centered log-ratio covariance matrix, which is approximately indistinguishable from the real basis covariance matrix as the dimensionality tends to infinity. The procedure can be viewed as adaptively thresholding the Median-of-Means estimator for the centered log-ratio covariance matrix. Thorough simulation studies are conducted to show the advantages of the proposed procedure.
报告人简介：
刘鹏飞，江苏师范大学副教授，主要研究方向为经验似然、贝叶斯统计和分位数回归等。
题目：Copula-based Partial Correlation Screening: a Joint and Robust Approach
报告人：夏小超 重庆大学
时间：2020.11.29 11：00-12：00
腾讯会议: ：112 255 466
报告摘要：
Screening for ultrahigh-dimensional features becomes difficult in the presence of outlying observations, heterogeneous or heavy-tailed distributions, multi-collinearity, and confounding effects. Standard correlation-based marginal screening methods may offer a weak solution to these problems. We contribute a novel robust joint screener that safeguards against outliers and distribution misspecification of both the response variable and the covariates, and accounts for external variables at the screening step. Specifically, we introduce a copula-based partial correlation (CPC) screener. We show that the empirical process of the estimated CPC converges weakly to a Gaussian process. Furthermore, we establish the sure screening property for the CPC screener under very mild technical conditions, which need not require a moment condition, and are weaker than existing alternatives in the literature. Moreover, from a theoretical perspective, our approach allows for a diverging number of conditional variables. Extensive simulation studies and two data applications demonstrate the effectiveness of the proposed screening method.
报告人简介：
夏小超，博士，重庆大学数学与统计学院教师，2015年获重庆大学统计学博士学位；2013年3月-2014年1月在澳门大学数学系交流访问(Research Assistant); 2017年7月-2018年7月在新加坡国立大学统计与应用概率系从事博士后(Research Fellow)研究。目前主持1项国家自然科学青年基金项目，主持完成1项湖北省自然科学基金项目和1项中央高校基金项目并结题。感兴趣的研究方向为高维统计数据分析、超高维特征筛选、模型平均、经验似然以及非参半参数回归模型。与合作者在国际统计学知名刊物Journal of Econometrics, Biometrics、Statistica Sinica、Scandinavian Journal of Statistics、CSDA等多个SCI杂志上发表和录用论文十余篇，担任美国数学会数学评论（Mathematical Reviews）评论员，为多个SCI杂志提供审稿服务
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