Exact adaptive confidence intervals for small areas.

@article{Burris2018ExactAC,
  title={Exact adaptive confidence intervals for small areas.},
  author={Kyle Burris and Peter D. Hoff},
  journal={arXiv: Methodology},
  year={2018}
}
In the analysis of survey data it is of interest to estimate and quantify uncertainty about means or totals for each of several non-overlapping subpopulations, or areas. When the sample size for a given area is small, standard confidence intervals based on data only from that area can be unacceptably wide. In order to reduce interval width, practitioners often utilize multilevel models in order to borrow information across areas, resulting in intervals centered around shrinkage estimators… Expand

Figures and Tables from this paper

Smaller p-Values via Indirect Information
TLDR
This article develops $p-values for evaluating means of normal populations that make use of indirect or prior information using a linking model through which indirect information about the mean of one population may be obtained from the data of other populations. Expand
Bayes-optimal prediction with frequentist coverage control
This article illustrates how indirect or prior information can be optimally used to construct a prediction region that maintains a target frequentist coverage rate. If the indirect information isExpand

References

SHOWING 1-10 OF 23 REFERENCES
The estimation of the mean squared error of small-area estimators
Abstract Small-area estimation has received considerable attention in recent years because of a growing demand for reliable small-area statistics. The direct-survey estimators, based only on the dataExpand
Adaptive multigroup confidence intervals with constant coverage
Confidence intervals for the means of multiple normal populations are often based on a hierarchical normal model. While commonly used interval procedures based on such a model have the nominalExpand
On parametric bootstrap methods for small area prediction
The particularly wide range of applications of small area prediction, e.g. in policy making decisions, has meant that this topic has received substantial attention in recent years. The problems ofExpand
SMALL AREA ESTIMATION USING ESTIMATED SAMPLING VARIANCES
In small area estimation, area level models such as the Fay-Herriot model (Fay and Herriot, 1979) are widely used to obtain efficient model-based estimators for small areas. The sampling variancesExpand
New important developments in small area estimation
TLDR
The purpose of this paper is to review and discuss some of the new important developments in small area estimation methods, covering both design-based and model-dependent methods, with the latter methods further classified into frequentist and Bayesian methods. Expand
Small area estimation: the EBLUP estimator based on spatially correlated random area effects
TLDR
A clear tendency in the empirical findings is that the introduction of spatially correlated random area effects reduce both the variance and the bias of the EBLUP estimator. Expand
A second-order efficient empirical Bayes confidence interval
We introduce a new adjusted residual maximum likelihood method (REML) in the context of producing an empirical Bayes (EB) confidence interval for a normal mean, a problem of great interest inExpand
sae: An R Package for Small Area Estimation
TLDR
This package can be used to obtain model-based estimates for small areas based on a variety of models at the area and unit levels, along with basic direct and indirect estimates. Expand
Estimates of Income for Small Places: An Application of James-Stein Procedures to Census Data
Abstract An adaptation of the James-Stein estimator is applied to sample estimates of income for small places (i.e., population less than 1,000) from the 1970 Census of Population and Housing. TheExpand
Spatio-Temporal Models in Small Area Estimation
A spatial r egression model in a general mixed ef fects model framework has been proposed for the small ar ea estimation problem. A common a utocorrelation pa rameter across the small areas has rExpand
...
1
2
3
...