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Composite Estimation for Single-Index Models with Responses Subject to Detection Limits
时间  Datetime
2017-12-15 15:00 — 16:00 
地点  Venue
Large Conference Room
报告人  Speaker
唐炎林
单位  Affiliation
同济大学
邀请人  Host
王成
报告摘要  Abstract

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 an human immunodeficiency virus antibody data set.

报告人介绍:唐炎林博士,同济大学数学科学学院副教授,2012年毕业于复旦大学统计系,师从朱仲义教授。曾于2009-2010到UIUC访问何旭铭教授一年,2015-2017到乔治华盛顿大学访问王会霞教授两年,也曾多次到香港中文大学访问宋心远教授。