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Activity Number:
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74
- Invited E-Poster Session I
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Type:
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Invited
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Date/Time:
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Sunday, August 7, 2022 : 8:30 PM to 9:25 PM
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Sponsor:
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IMS
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Abstract #322768
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Title:
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Spatially Adaptive False Discovery Rate Thresholding for Sparse Estimation
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Author(s):
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Gourab Mukherjee* and Wen Sun and Jiajun Luo
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Companies:
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University of Southern California and Zhejiang University and Linkedin
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Keywords:
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FDR thresholding;
Structured multiple testing;
Spatial analysis;
BH procedure
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Abstract:
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We develop a new false discovery rate (FDR) based threshold estimator by extending the elegant FDR based estimator in Abramovich et al., 2006 to spatial settings. The idea is to first construct robust and structure-adaptive weights by estimating local sparsity levels, and thereafter to set spatially adaptive thresholds using the weighted Benjamini-Hochberg procedure. We present asymptotic results demonstrating the superior performance of the proposed method. Through numerical experiments we illustrate the importance of spatial adaptation by studying the finite sample performance of the proposed estimator.
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Authors who are presenting talks have a * after their name.
