文献详情
Test for conditional independence with application to conditional screening
文献类型：期刊
通讯作者：Zhu, LP (reprint author), Renmin Univ China, Inst Stat & Big Data, Ctr Appl Stat, Beijing 100872, Peoples R China.
期刊名称：JOURNAL OF MULTIVARIATE ANALYSIS影响因子和分区
年：2020
卷：175
ISSN：0047-259X
关键词：Conditional independence; Feature screening; High dimensional data; Independence; Sure screening property
所属部门：统计与大数据研究院
摘要：Measuring and testing conditional dependence are fundamental problems in statistics. Imposing mild conditions on Rosenblatt transformations (Rosenblatt, 1952), we establish an equivalence between the conditional and unconditional independence, which appears to be entirely irrelevant at the first glance. Such an equivalence allows us to convert the problem of testing conditional independence into the problem of testing unconditional independence. We further adopt the Blum-Kiefer-Rosenblatt correl ...More
Measuring and testing conditional dependence are fundamental problems in statistics. Imposing mild conditions on Rosenblatt transformations (Rosenblatt, 1952), we establish an equivalence between the conditional and unconditional independence, which appears to be entirely irrelevant at the first glance. Such an equivalence allows us to convert the problem of testing conditional independence into the problem of testing unconditional independence. We further adopt the Blum-Kiefer-Rosenblatt correlation (Blum et al., 1961) to develop a test for conditional independence, which is powerful to capture nonlinear dependence and is robust to heavy-tailed errors. We obtain explicit forms for the asymptotic null distribution which involves no unknown tunings, rendering fast and easy implementation of our test for conditional independence. With this conditional independence test, we further propose a conditional screening method for high dimensional data to identify truly important covariates whose effects may vary with exposure variables. We use the false discovery rate to determine the screening cutoff. This screening approach possesses both the sure screening and the ranking consistency properties. We illustrate the finite sample performances through simulation studies and an application to the gene expression microarray dataset. (C) 2019 Elsevier Inc. All rights reserved. ...Hide

DOI：10.1016/j.jmva.2019.104557
百度学术：Test for conditional independence with application to conditional screening
语言：外文
基金：National Key Research and Development Program of China [2018YFC0830301]; Beijing Natural Science Foundation, ChinaBeijing Natural Science Foundation [Z19J00009]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [11731011, 11771361, 11671334, 11871409, 11931014]; Ministry of Education, China of Key Research Institute of Humanities and Social Sciences at Universities [16JJD910002]
作者其他论文



MODEL-FREE FEATURE SCREENING FOR ULTRAHIGH DIMENSIONAL DATATHROUGH A MODIFIED BLUM-KIEFER-ROSENBLATT CORRELATION.Zhou, Yeqing, Zhu, Liping,.STATISTICA SINICA. 2018, 28(3), 1351-1370.
A ROBUST AND EFFICIENT APPROACH TO CAUSAL INFERENCE BASED ON SPARSE SUFFICIENT DIMENSION REDUCTION.Ma, Shujie, Zhu, Liping, Zhang, Zhiwei, et al. .ANNALS OF STATISTICS. 2019, 47(3), 1505-1535.
SEMIPARAMETRIC ESTIMATION AND INFERENCE OF VARIANCE FUNCTION WITH LARGE DIMENSIONAL COVARIATES.Ma, Yanyuan, Zhu, Liping,.STATISTICA SINICA. 2019, 29(2), 567-588.
NONLINEAR INTERACTION DETECTION THROUGH MODEL-BASED SUFFICIENT DIMENSION REDUCTION.Fan, Guoliang, Zhu, Liping, Ma, Shujie,.STATISTICA SINICA. 2019, 29(2), 917-937.
Model-free conditional feature screening with exposure variables.Zhou, Yeqing, Liu, Jingyuan, Hao, Zhihui, et al. .STATISTICS AND ITS INTERFACE. 2019, 12(2), 239-251.