Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A General Framework for Empirical Bayes Estimation in Discrete Linear
  Exponential Family   We develop a Nonparametric Empirical Bayes (NEB) framework for compound
estimation in the discrete linear exponential family, which includes a wide
class of discrete distributions frequently arising from modern big data
applications. We propose to directly estimate the Bayes shrinkage factor in the
generalized Robbins' formula via solving a scalable convex program, which is
carefully developed based on a RKHS representation of the Stein's discrepancy
measure. The new NEB estimation framework is flexible for incorporating various
structural constraints into the data driven rule, and provides a unified
approach to compound estimation with both regular and scaled squared error
losses. We develop theory to show that the class of NEB estimators enjoys
strong asymptotic properties. Comprehensive simulation studies as well as
analyses of real data examples are carried out to demonstrate the superiority
of the NEB estimator over competing methods.

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The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci