Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Smoothing, Clustering, and Benchmarking for Small Area Estimation   We develop constrained Bayesian estimation methods for small area problems:
those requiring smoothness with respect to similarity across areas, such as
geographic proximity or clustering by covariates; and benchmarking constraints,
requiring (weighted) means of estimates to agree across levels of aggregation.
We develop methods for constrained estimation decision-theoretically and
discuss their geometric interpretation. Our constrained estimators are the
solutions to tractable optimization problems and have closed-form solutions.
Mean squared errors of the constrained estimators are calculated via
bootstrapping. Our techniques are free of distributional assumptions and apply
whether the estimator is linear or non-linear, univariate or multivariate. We
illustrate our methods using data from the U.S. Census's Small Area Income and
Poverty Estimates program.

33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
1
The categories of this paper are: cs
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess