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Activity Number:
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404
- Recent Research in High-Dimensional and Complex Data Analysis
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Type:
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Topic-Contributed
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Date/Time:
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Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #317326
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Title:
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An Ensemble of Inverse Moments Estimator for Sufficient Dimension Reduction
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Author(s):
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Qin Wang* and Yuan Xue
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Companies:
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University of Alabama and University of International Business and Economics
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Keywords:
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Central subspace;
Ensemble estimation;
Sufficient dimension reduction
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Abstract:
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Sufficient dimension reduction (SDR) is known to be a useful tool in data visualization and information retrieval for high dimensional data. Many well-known SDR approaches investigate the inverse conditional moments of the predictors given the response. Motivated by the idea of the aggregate dimension reduction, we propose an ensemble of inverse moment estimators to explore the central subspace. The new approach can substantially improve the estimation accuracy for the directions beyond the regression mean functions. A ladle estimator is proposed to determine the structural dimension of the central subspace. Both simulation studies and a real data application show the efficacy of the newly proposed method.
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Authors who are presenting talks have a * after their name.
