Motivation: Experiments at the Census Bureau are used to answer many research questions, especially those related to testing, evaluating, and advancing survey sampling methods. A properly designed experiment provides a valid, cost-effective framework that ensures the right type of data is collected as well as sufficient sample sizes and power are attained to address the questions of interest. The use of valid statistical models is vital to both the analysis of results from designed experiments and in characterizing relationships between variables in the vast data sources available to the Census Bureau. Statistical modeling is an essential component for wisely integrating data from previous sources (e.g., censuses, sample surveys, and administrative records) in order to maximize the information that they can provide. In particular, linear mixed effects models are ubiquitous at the Census Bureau through applications of small area estimation. Models can also identify errors in data, e.g. by computing valid tolerance bounds and flagging data outside the bounds for further review.
Accomplishments (October 2017 - September 2018):
Short-Term Activities (FY 2019):
Longer-Term Activities (beyond FY 2019):
Gamage, G., Mathew, T., and Weerahandi, S. (2013). “Generalized Prediction Intervals for BLUPs in Mixed Models,” Journal of Multivariate Analysis, 120, 226-233.
Heim, K. and Raim, A.M. (2016). Predicting coverage error on the Master Address File using spatial modeling methods at the block level. In JSM Proceedings, Survey Research Methods Section. Alexandria, VA: American Statistical Association.
Klein, M., Mathew, T. and Sinha, B. K. (2014). “Likelihood Based Inference Under Noise Multiplication,” Thailand Statistician. 12(1), pp.1-23. URL: http://www.tci-thaijo.org/index.php/thaistat/article/view/34199/28686
Mathew, T. and Young, D. S. (2013). “Fiducial-Based Tolerance Intervals for Some Discrete Distributions,” Computational Statistics and Data Analysis, 61, 38-49.
Mathew, T., Menon, S., Perevozskaya, I. and Weerahandi, S. (2016). “Improved Prediction Intervals in Heteroscedastic Mixed-Effects Models,” Statistics & Probability Letters, 114, 48-53.
Morris, D.S., Sellers, K.F., and Menger, A. (2017) Fitting a Flexible Model for Longitudinal Count Data Using the NLMIXED Procedure, SAS Global Forum Proceedings Paper 202-2017, SAS Institute: Cary, NC.
Raim, A.M. and Gargano, M.N. (2015). “Selection of predictors to model coverage errors in the Master Address File,” Research Report Series: Statistics #2015-04, Center for Statistical Research and Methodology, U.S. Census Bureau.
Raim, A.M. (2016). Informing maintenance to the U.S. Census Bureau's Master Address File with statistical decision theory. In JSM Proceedings, Government Statistics Section. Alexandria, VA: American Statistical Association.
Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley, and Christopher K. Wikle (2017). “A Model Selection Study for Spatio-Temporal Change of Support,” in Proceedings, Government Statistics Section of the American Statistical Association, Alexandria, VA: American Statistical Association.
Sellers, K., Lotze, T., and Raim, A. (2017) COMPoissonReg: Conway-Maxwell-Poisson Regression, version 0.4.0, 0.4.1, https://cran.r-project.org/web/packages/COMPoissonReg/index.html
Sellers, K.F., and Morris, D. (In Press). “Under-dispersion Models: Models That Are ‘Under The Radar’”, Communications in Statistics – Theory and Methods.
Sellers, K.F., Morris, D.S., and Balakrishnan, N. (2016). “Bivariate Conway-Maswell-Poisson Distribution: Formulation, Properties, and Inference,” Journal of Multivariate Analysis, 150:152-168.
Sellers K.F., Morris D.S., Shmueli, G., and Zhu, L. (2017). “Reply: Models for Count Data (a response to a letter to the editor), The American Statistician.
Sellers, K.F. and Raim, A.M. (2016). "A flexible zero-inflated model to address data dispersion". Computational Statistics and Data Analysis, 99: 68-80.
Sellers, K., Morris, D., Balakrishnan, N., Davenport, D. (2017) multicmp: Flexible Modeling of Multivariate Count Data via the multivariate Conway-Maxwell-Poisson distribution, https://cran.r-project.org/web/packages/multicmp/index.html
Young, D.S. (2013). “Regression Tolerance Intervals,” Communications in Statistics – Simulation and Computation, 42(9), 2040-2055.
Young, D.S. (2014), "A procedure for approximate negative binomial tolerance intervals", Journal of Statistical Computation and Simulation, 84(2), pp.438-450. URL: http://dx.doi.org/10.1080/00949655.2012.715649
Young, D. and Mathew, T. (2015). “Ratio Edits Based on Statistical Tolerance Intervals.” Journal of Official Statistics 31, 77-100.
Young, D.S., Raim, A.M., and Johnson, N.R. (2017). "Zero-inflated modelling for characterizing coverage errors of extracts from the U.S. Census Bureau's Master Address File". Journal of the Royal Statistical Society: Series A. 180(1):73-97.
Zhu, L., Sellers, K.F., Morris, D.S., and Shmueli, G. (2017) Bridging the Gap: A Generalized Stochastic Process for Count Data, The American Statistician, 71 (1): 71-80.
Zhu, L., Sellers, K., Morris, D., Shmueli, G.,and Davenport, D. (2017) cmpprocess: Flexible Modeling of Count Processes, https://cran.r-project.org/web/packages/cmpprocess/index.html
Contact: Andrew Raim, Thomas Mathew, Kimberly Sellers, Dan Weinberg, Robert Ashmead, Scott Holan (R&M)
Funding Sources for FY 2018: