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Activity Number: 302 - Statistical Methods for Data Integration
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: International Chinese Statistical Association
Abstract #310963
Title: Robust Integrative Regression Analysis of High-Dimensional Heterogeneous Data
Author(s): Xiaoli Gao* and Bin Luo
Companies: University of North Carolina At Greensboro and University of North Carolina at Greensboro
Keywords: Integrative study; Robust estimation; Variable selection; High-dimensional; Heterogeneous; Sparisity

Multiple heterogeneous integrative study is very challenging in high-dimensional integrative settings, especially when data have heavy-tailed distribution or outliers exist in random errors and covariates. Under ultra-high dimensional sparse regression models, we propose a novel robust integrative estimation procedure by aggregating local high-dimensional redescending M estimators in this paper. In theory, we provide some sufficient conditions under which the aggregated redescending M estimators possesses consistent variable selection result. The finite-sample performance of the proposed procedure is studied via extensive simulations and two real data integrative studies.

Authors who are presenting talks have a * after their name.

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