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Activity Number:
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520
- Variable Selection, Model Selection, and Aggregated Inference
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Type:
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Contributed
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Date/Time:
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Thursday, August 11, 2022 : 8:30 AM to 10:20 AM
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Sponsor:
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International Chinese Statistical Association
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Abstract #323043
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Title:
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Minimum Discrepancy Approach for Dimension Reduction by Feature Filter
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Author(s):
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Pei Wang*
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Companies:
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Miami of Ohio
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Keywords:
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Dimension reduction ;
variable selection ;
feature filter
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Abstract:
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The minimum discrepancy approach is useful in sufficient dimension reduction (SDR). In this article, we develop a novel SDR method through a minimum discrepancy approach using characteristic function. To obtain the sparse solution, a regularization method is proposed. The asymptotic results are established and the estimation method for determining structural dimension is provided. We demonstrate the efficacy of our method through extensive simulations and a real data example.
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Authors who are presenting talks have a * after their name.
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