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.
Franco, C. and Bell, W.R. (In Press). “Using American Community Survey Data to Improve Estimates from Smaller U.S. Surveys through Bivariate Small Area Estimation Models,” Journal of Survey Statistics and Methodology.
Parker, P.A., Janicki, R., and Holan, S. (In Press). “Bayesian Methods Applied to Small Area Estimation for Establishment Statistics,” in Bavdaž, M., Bender, S., Jones, J., MacFeely, S., Sakshaug, J.W., Thompson, K.J., and van Delden, A. (Eds.), Advances in Business Statistics, Methods and Data Collection, Wiley.
Parker, P., Holan, S., and Janicki, R. (2022). “Computationally Efficient Bayesian Unit-level Models for Non-Gaussian Data Under Informative Sampling with Application to Estimation of Health Insurance Coverage,” The Annals of Applied Statistics, Vol 16, No. 2, 887-904.
Ghosh, T., Ghosh, M., Maples, J., and Tang, X. (2022). "Multivariate Global-Local Priors for Small Area Estimation," STATS, v5, 673-688. https://www.mdpi.com/2571-905X/5/3/40/htm.
Janicki, R., Raim, A.M., Holan, S.H., and Maples, J. (2022). “Bayesian Nonparametric Multivariate Spatial Mixture Mixed Effects Models with Application to American Community Survey Special Tabulations,” The Annals of Applied Statistics, Volume 16, Issue 1, 144-168.
Erciulescu, A., Franco, C., and Lahiri, P. (2021). “Use of Administrative Records in Small Area Estimation,” in Chun, A. Y. and Larsen, M. (Eds.), Administrative Records for Survey Methodology, New York, NY: Wiley Publishers.
Liu, B., Dompreh, I., and Hartman, A.M. (2021). “Small Area Estimation of Smoke-Free Workplace Policies and Home Rules in U.S. Counties,” Journal of Nicotine and Tobacco Research.
Parker, P. A., Holan, S. H., and Janicki, R. (2020). “Bayesian Unit-Level Modeling of Count Data under Informative Sampling Designs,” Stat, 9.
Maples, J. (2019). “Small Area Estimates of the Child Population and Poverty in School Districts Using Dirichlet-Multinomial Models,” 2019 Proceedings of the American Statistical Association, Section on Survey Research Methods, American Statistical Association, Alexandria, VA, 3150-3152.
Bell, W. R., Chung, H. C., Datta, G. S., and Franco, C. (2019). “Measurement Error in Small Area Estimation: Functional vs. Structural vs. Naïve Models,” Survey Methodology, 45, 61-80.
Chakraborty, A., Datta, G.S., and Mandal, A. (2019). “Robust Hierarchical Bayes Small Area Estimation for Nested Error Regression Model,” International Statistical Review, 87, S1, S158–S176, doi:10.1111/insr.12283.
Chung, H., Datta, G., and Maples, J. (2019). “Estimation of Median Incomes of the American States: Bayesian Estimation of Means of Subpopulations,” Opportunities and Challenges in Development, Simanti Bandyopadhyay and Mousumi Datta (ed.), New York: Springer, 505-518.
Franco, C., Little, R. J. A., Louis, T. A., and Slud, E. V. (2019). “Comparative Study of Confidence Intervals for Proportions in Complex Surveys,” Journal of Survey Statistics and Methodology, 7, 3, 334-364.
Datta, G.S., Rao, J.N.K., Torabi, M., and Liu, B. (2018). “Small Area Estimation with Multiple Covariates Measured with Errors: A Nested Error Linear Regression Approach of Combining Two Surveys,” Journal of Multivariate Analysis, 167, 49-59.
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.
Janicki, R. and Vesper, A. (2017). “Benchmarking Techniques for Reconciling Small Area Models at Distinct Geographic Levels,” Statistical Methods Applications, DOI: https://doi.org/10.1007/s10260-017-0379-x, 26, 557-581.
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.
Chakraborty, A., Datta, G.S., and Mandal, A. (2016). “A Two-component Normal Mixture Alternative to the Fay-Herriot Model,” Joint issue of Statistics in Transition new series and Survey Methodology, Part II, 17, 67-90.
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, D.C.
Datta, G.S. and Mandal, A. (2015). “Small Area Estimation with Uncertain Random Effects,” Journal of the American Statistical Association: Theory and Methods, 110, 1735-1744.
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.
Bell, W.R., Datta, G.S., and Ghosh, M. (2013). “Benchmarking Small area Estimators,” Biometrika, 100, 189-202, doi:10.1093/biomet/ass063.
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.
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.
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.
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, D.C.
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.
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, D.C.
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.
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.
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, D.C.
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.
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.
Jerry Maples, Ryan Janicki, Gauri Datta, Kyle Irimata, Bill Bell (ADRM), Eric Slud
0331 – Working Capital Fund / General Research Project
Various Decennial, Demographic, and Economic Projects