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