Small area statistics: large statistical problems.

Small area statistics: large statistical problems.

Bayesian Statistics for Small Area Estimation

Abstract. National statistical offices are often required to provide statistical information about characteristics of the population, such as mean income or unemployment rate, at several administrative or small area levels. Having good area level estimates is important because policies will often be based on this type of information. In this paper we describe how Bayesian hierarchical models ca...

Small Area Research for the Occupational Employment Statistics Survey

The Occupational Employment Statistics (OES) Survey is a yearly mail survey designed to produce estimates of employment and wages for more than 700 occupations in the U.S. The OES sample is stratified by geographic area, economic activity, and employment size class; with geographic area defined by State, Metropolitan Statistical Area (MSA) and balance of State area, and economic activity define...

Assessing genome-wide statistical significance for large p small n problems.

Assessing genome-wide statistical significance is an important issue in genetic studies. We describe a new resampling approach for determining the appropriate thresholds for statistical significance. Our simulation results demonstrate that the proposed approach accurately controls the genome-wide type I error rate even under the large p small n situations.

On Assessing Genome-Wide Statistical Significance for Large p Small n Problems

Assessing genome-wide statistical significance is an important issue in genetic studies. We describe a new resampling approach for determining the appropriate thresholds for statistical significance. Our simulation results demonstrate that the proposed approach accurately controls the genome-wide type I error rate even under the large p small n situations.