Small-Area Confidence Bounds on Small Cell Proportions in Survey Populations

• Activity Number: 66
• Type: Topic Contributed
• Date/Time: Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Eric Slud*+ and Aaron Gilary and Jerry Maples
• Companies: U.S. Census Bureau and U.S. Census Bureau and U.S. Census Bureau
• Keywords: confidence bounds ; prediction intervals ; arcsin square root transformation ; beta-binomial regression ; mixed-effect logistic regression ; transformed Fay-Herriot model

Abstract

Motivated by the problems of `quality filtering' of estimated counts in American Community Survey (ACS) tables, and of reporting small-domain coverage results from the Census Coverage Measurement (CCM) program, this paper studies methods for placing confidence bounds on proportions for cells and tables, estimated from complex surveys, in which the estimated counts are zeroes. While coefficients of variation are generally used in measuring the quality of estimated counts, they do not make sense for assessing validity of very small estimated counts. The problem is formulated here in terms of (upper) confidence bounds for unknown proportions. We discuss methods of creating confidence bounds from small-area models including logistic, beta-binomial, and variance-stabilized (arcsin square root transformed) linear models. The model-based confidence bounds are compared with single-cell bounds de