Spatially Adaptive False Discovery Rate Thresholding for Sparse Estimation

报告人：孙文光

报告地点：腾讯会议ID:691 570 270

报告时间：2022年10月12日星期三10:00-11:00

报告摘要：

Exploiting spatial information in high-dimensional inference promises to improve the accuracy of statistical procedures. This article develops a new class of spatially adaptive false discovery rate thresholding (SAFT) procedure by extending the elegant false discovery rate thresholding estimator (Abramovich et al., 2006) to spatial settings. The idea is first constructing robust and structured-adaptive weights via estimating the local sparsity levels, and then setting spatially adaptive thresholds through weighted Benjamini-Hochberg (BH) Procedure. SAFT procedure is data-driven and assumption- lean. Theoretical results demonstrate the superior asymptotic performance over the original false discovery rate thresholding estimator in spatial settings. The finite sample performance is studied using both simulated data and real data, which shows the proposed SAFT procedure outperforms the existing methods in various settings. Joint work with Jiajun Luo, Gourab Mukherjee and Yunjin Choi.

主讲人简介：

孙文光是浙江大学求是讲席教授和数据科学研究中心主任。回国前是美国南加州大学（USC）马绍尔商学院数据科学与运筹系教授。主要研究方向为大规模统计推断、整合分析和迁移学习、共形预测、选择性推断和统计决策理论。曾获美国科学基金会CAREER Award，USC商学院杰出研究奖（2次）和Golden Apple最佳教学奖, 担任JRSS-B及Journal of Multivariate Analysis的副主编。