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Activity Number: 288 - New Insights from Classical Wisdom—honoring Lawrence D. Brown’s Contributions to Graduate Student Education
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract #304168
Title: Nonparametric Empirical Bayes Methods for Sparse, Noisy Signals
Author(s): Junhui Cai* and Linda Zhao
Companies: and University of Pennsylvania
Keywords: nonparametric empirical bayes; FDR

We consider the high dimensional signal recovering problems. The goal is to identify the true signals from the noise controlling FDR and then to make inference for the unknown signals. We propose to use Nonparametric Empirical Bayesian schemes to tackle the problem. The method adapts well to varying degrees of sparsity. It not only performs well to recover the signals, but also provides credible intervals. The method is built upon noisy data with exponential family distribution. It covers large range of data structure such as normal means with heteroskedastic variance, Poisson data with varying degrees of frequency, and Binomial counts. Simulations show that our method outperforms existing ones. Applications in microarray data as well as sport data such as predicting batting averages in L. Brown (2008) will be discussed. Brown, L. D. and Greenshtein, E. (2009) and Brown, L.D., Greenshtein, E., and Ritov, Y. (2013) are related but our approaches are different.

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

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