Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonparametric Empirical Bayes Estimation and Testing for Sparse and
  Heteroscedastic Signals   Large-scale modern data often involves estimation and testing for
high-dimensional unknown parameters. It is desirable to identify the sparse
signals, ``the needles in the haystack'', with accuracy and false discovery
control. However, the unprecedented complexity and heterogeneity in modern data
structure require new machine learning tools to effectively exploit
commonalities and to robustly adjust for both sparsity and heterogeneity. In
addition, estimates for high-dimensional parameters often lack uncertainty
quantification. In this paper, we propose a novel Spike-and-Nonparametric
mixture prior (SNP) -- a spike to promote the sparsity and a nonparametric
structure to capture signals. In contrast to the state-of-the-art methods, the
proposed methods solve the estimation and testing problem at once with several
merits: 1) an accurate sparsity estimation; 2) point estimates with
shrinkage/soft-thresholding property; 3) credible intervals for uncertainty
quantification; 4) an optimal multiple testing procedure that controls false
discovery rate. Our method exhibits promising empirical performance on both
simulated data and a gene expression case study.

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The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
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The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
24
The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
3
The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: adap-org