Motivation: Small area estimation is important in light of a continual demand by data users for finer geographic detail of published statistics and for various subpopulations. Traditional demographic sample surveys designed for national estimates do not provide large enough samples to produce reliable direct estimates for small areas such as counties and even most states. The use of valid statistical models can provide small area estimates with greater precision; however, bias due to an incorrect model or failure to account for informative sampling can result.
Accomplishments (October 2017 - September 2018):
Short-Term Activities (FY 2019):
Longer-Term Activities (beyond FY 2019):
Arima, S., Bell, W. R., Datta, G. S., Franco, C., and Liseo, B. (2017). “Multivariate Fay-Herriot Bayesian Estimation of Small Area Means Under Functional Measurement Error,” Journal of the Royal Statistical Society--Series A, 180(4), 1191-1209.
Bell, W. R., Chung, H. C., Datta, G. S., and Franco, C. (In Press). “Measurement Error in Small Area Estimation: Functional vs. Structural Vs. Naïve Models," Survey Methodology.
Franco, C. and Bell, W. R. (2013). “Applying Bivariate/Logit Normal Models to Small Area Estimation,” In JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association. 690-702.
Franco, C. and Bell, W. R. (2015). “Borrowing information over time in binomial/logit normal models for small area estimation,” Joint issue of Statistics in Transition and Survey Methodology, 16, 4, 563-584.
Franco, C., Little, R. J. A., Louis, T. A., and Slud, E. V. (2018). “Comparative Studies of Confidence Intervals for Proportions in Complex Surveys,” Journal of Survey Statistics and Methodology.
Datta, G., Ghosh, M., Steorts, R., and Maples, J. (2011). “Bayesian Benchmarking with Applications to Small Area Estimation,” TEST, Volume 20, Number 3, 574-88.
Huang, E., Malec, D., Maples J., and Weidman, L. (2007). “American Community Survey (ACS) Variance Reduction of Small Areas via Coverage Adjustment Using an Administrative Records Match,” Proceedings of the 2006 Joint Statistical Meetings, American Statistical Association, Alexandria, VA, 3150-3152.
Janicki, R. (2011). “Selection of prior distributions for multivariate small area models with application to small area health insurance estimates.” JSM Proceedings, Government Statistics Section. American Statistical Association, Alexandria, VA.
Janicki, R. and Vesper, A. (2017). "Benchmarking Techniques for Reconciling Small Area Models at Distinct Geographic Levels." Statististical Methods Applications, DOI: https://doi.org/10.1007/s10260-017-0379-x, 26, 557-581.
Janicki, R (2016). "Estimation of the difference of small area parameters from different time periods". Research Report Series (Statistics #2016-01), Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, DC.
Joyce, P. and Malec, D. (2009). “Population Estimation Using Tract Level Geography and Spatial Information,” Research Report Series (Statistics #2009-3), Statistical Research Division, U.S. Census Bureau, Washington, DC.
Malec, D. (2005). “Small Area Estimation from the American Community Survey Using a Hierarchical Logistic Model of Persons and Housing Units,” Journal of Official Statistics, 21 (3), 411-432.
Malec, D. and Maples, J. (2008). “Small Area Random Effects Models for Capture/Recapture Methods with Applications to Estimating Coverage Error in the U.S. Decennial Census,” Statistics in Medicine, 27, 4038-4056.
Malec, D. and Müller, P. (2008). “A Bayesian Semi-Parametric Model for Small Area Estimation,” in Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh (eds. S. Ghoshal and B. Clarke), Institute of Mathematical Statistics, 223-236.
Maples, J. and Bell, W. (2007). “Small Area Estimation of School District Child Population and Poverty: Studying Use of IRS Income Tax Data,” Research Report Series (Statistics #2007-11), Statistical Research Division, U.S. Census Bureau, Washington, DC.
Maples, J. (2011). “Using Small-Area Models to Improve the Design-Based Estimates of Variance for County Level Poverty Rate Estimates in the American Community Survey,” Research Report Series (Statistics #2011-02), Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, DC.
Maples, J. (2017). “Improving Small Area Estimates of Disability: Combining the American Community Survey with the Survey of Income and Program Participation,” Journal of the Royal Statistical Society-Series A, 180(4), 1211-1227.
Slud, E. and Maiti, T. (2006). “Mean-Squared Error Estimation in Transformed Fay-Herriot Models,” Journal of the Royal Statistical Society-Series B, 239-257.
Slud, E. and Maiti, T. (2011). “Small-Area Estimation Based on Survey Data from Left-Censored Fay-Herriot Model,” Journal of Statistical Planning & Inference, 3520-3535.
Contact: Jerry Maples, Ryan Janicki, Carolina Franco, Gauri Datta, Kyle Irimata, Bill Bell (R&M), Eric Slud
Funding Sources for FY 2018: