文献详情
SEMIPARAMETRIC ESTIMATION AND INFERENCE OF VARIANCE FUNCTION WITH LARGE DIMENSIONAL COVARIATES
文献类型：期刊
通讯作者：Ma, YY (reprint author), Penn State Univ, Dept Stat, University Pk, PA 16802 USA.
期刊名称：STATISTICA SINICA影响因子和分区
年：2019
卷：29
期：2
页码：567-588
ISSN：1017-0405
关键词：Central mean subspace; central variance subspace; dimension reduction; location-scale family; semiparametric efficiency
所属部门：统计与大数据研究院
摘要：We investigate the simultaneous estimation and inference of the central mean subspace and central variance subspace to reduce the effective number of covariates that predict, respectively, the mean and variability of the response variable. We study the estimation, inference and efficiency properties under different scenarios, and further propose a class of locally efficient estimators when the truly efficient estimator is not practically available. This partially explains the necessity of some d ...More
We investigate the simultaneous estimation and inference of the central mean subspace and central variance subspace to reduce the effective number of covariates that predict, respectively, the mean and variability of the response variable. We study the estimation, inference and efficiency properties under different scenarios, and further propose a class of locally efficient estimators when the truly efficient estimator is not practically available. This partially explains the necessity of some dimension-reduction assumptions that are commonly imposed on the conditional mean function in estimating the central variance subspace. Comprehensive simulation studies and a data analysis are performed to demonstrate the finite sample performance and efficiency gain of the locally efficient estimators in comparison with existing estimation procedures. ...Hide

DOI：10.5705/ss.202017.0084
百度学术：SEMIPARAMETRIC ESTIMATION AND INFERENCE OF VARIANCE FUNCTION WITH LARGE DIMENSIONAL COVARIATES
语言：外文
人气指数：2
浏览次数：2
基金：National Science FoundationNational Science Foundation (NSF) [DMS-1608540]; National Institute of Neurological DisordersUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [R01-NS073671]; National Natural Science Foundation of P. R. ChinaNational Natural Science Foundation of China [11371236, 11422107, 11731011]; Ministry of Education Project of Key Research Institute of Humanities and Social Sciences at Universities [16JJD910002]; National Youth Top-notch Talent Support Program, P. R. China
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