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Activity Number: 11 - Daunting Challenges and Innovative Solutions for Big Data Analysis
Type: Invited
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #326902 Presentation
Title: Nonparametric Empirical Bayes Methods for High Dimension Problems 
Author(s): Linda Zhao* and Junhui Cai
Companies: University of Pennsylvania and University of Pennsylvania
Keywords: Empirical Bayes; High Dimensional Data; Sparsity; FDR

Genomic data often comes in a form which is noisy and sparse. It is challenging to recover the truth due to its complex structure and high dimensionality.

We propose to use Nonparametric Empirical Bayesian schemes to tackle the problem. The method adapts especially well to varying degrees of sparsity. It not only performs well to recover the signals, but also provides credible intervals. We also propose a method to control FDR in the case of multiple testing.

Joint work with J. Cai and Y. Ritov

Authors who are presenting talks have a * after their name.

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