题目：Composite Estimation for Single-Index Models with Responses Subject to Detection Limits
报告人：唐炎林 华东师范大学 研究员
报告时间：2019年9月17日（周二）下午15:00
报告地点：信息楼407室
报告摘要：We propose a semiparametric estimator for single-index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the nonparametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors, but much more efficient for heavy-tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.
报告人简介：唐炎林，华东师范大学统计学院研究员。2012年1月于复旦大学统计系获得博士学位，师从朱仲义教授。2012年5月起在同济大学数学系工作，历任讲师、副教授，2019年1月加入华东师范大学统计学院。读博期间曾在UIUC何旭铭教授处联合培养一年，2015-2017在GWU进行为期两年的公派博士后研究(合作导师：王会霞教授)，2011-2019期间曾多次访问香港中文大学宋心远教授。唐教授的主要研究方向包括分位数回归、高维数据统计推断、删失数据，先后主持国家自然科学基金面上项目、青年项目各一项，上海市浦江人才计划一项，目前在Biometrika、Statistica Sinica、Biometrics等国际知名SCI期刊发表论文二十余篇。
报告人：唐炎林 华东师范大学 研究员
报告时间：2019年9月17日（周二）下午15:00
报告地点：信息楼407室
报告摘要：We propose a semiparametric estimator for single-index models with censored responses due to detection limits. In the presence of left censoring, the mean function cannot be identified without any parametric distributional assumptions, but the quantile function is still identifiable at upper quantile levels. To avoid parametric distributional assumption, we propose to fit censored quantile regression and combine information across quantile levels to estimate the unknown smooth link function and the index parameter. Under some regularity conditions, we show that the estimated link function achieves the nonparametric optimal convergence rate, and the estimated index parameter is asymptotically normal. The simulation study shows that the proposed estimator is competitive with the omniscient least squares estimator based on the latent uncensored responses for data with normal errors, but much more efficient for heavy-tailed data under light and moderate censoring. The practical value of the proposed method is demonstrated through the analysis of a human immunodeficiency virus antibody data set.
报告人简介：唐炎林，华东师范大学统计学院研究员。2012年1月于复旦大学统计系获得博士学位，师从朱仲义教授。2012年5月起在同济大学数学系工作，历任讲师、副教授，2019年1月加入华东师范大学统计学院。读博期间曾在UIUC何旭铭教授处联合培养一年，2015-2017在GWU进行为期两年的公派博士后研究(合作导师：王会霞教授)，2011-2019期间曾多次访问香港中文大学宋心远教授。唐教授的主要研究方向包括分位数回归、高维数据统计推断、删失数据，先后主持国家自然科学基金面上项目、青年项目各一项，上海市浦江人才计划一项，目前在Biometrika、Statistica Sinica、Biometrics等国际知名SCI期刊发表论文二十余篇。