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 are 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.
Feng, X., Mathew, T, and Adragni, K. (2021). “Interval Estimation of the Intra-class Correlation in General Linear Mixed Effects Models,” Journal of Statistical Theory and Practice, 15, Article 65.
Sellers, K.F., Arab, A., Melville, S., and Cui, F. (2021). “A Flexible Univariate Moving Average Time-Series Model for Dispersed Count Data,” Journal of Statistical Distributions and Applications 8 (1). https://doi.org/10.1186/s40488-021-00115-2
Sellers, K.F., Li, T., Wu, Y., and Balakrishnan, N. (2021). “A Flexible Multivariate Distribution for Correlated Count Data,” Stats, 4(2), 308-326, https://doi.org/10.3390/stats4020021.
Zhao, J., Mathew, T., and Bebu, I. (2021). “Accurate Confidence Intervals for Inter-Laboratory Calibration and Common Mean Estimation,” Chemometrics and Intelligent Laboratory Systems, 208. DOI: 10.1016/j.chemolab.2020.104218.
Zimmer, Z., Park, D., and Mathew, T. (2021). “Tolerance Limits under Zero-Inflated Lognormal and Gamma Distributions,” Computational and Mathematical Methods, Special Issue on Statistics, 3. DOI: 10.1002/cmm4.1113.
Morris, D.S., Raim, A.M., and Sellers, K.F. (In Press). “A Conway-Maxwell-multinomial Distribution for Flexible Modeling of Clustered Categorical Data,” Journal of Multivariate Analysis. DOI: https://doi.org/10.1016/j.jmva.2020.104651.
Raim, A.M., Holan, S.H., Bradley, J.R., and Wikle, C.K. (2020). stcos: “Space-Time Change of Support, version 0.3.0,” https://cran.r-project.org/package=stcos.
Sellers K.F., Peng, S.J., and Arab, A. (2020). “A Flexible Univariate Autoregressive Time-series Model for Dispersed Count Data,” Journal of Time Series Analysis, 41(3): 436-453.
Zhu, L., Sellers, K., Morris, D., Shmueli, G., and Davenport, D. (2020). cmpprocess: “Flexible Modeling of Count Processes,” version 1.1, https://cran.r-project.org/package=cmpprocess
Raim, A.M., Holan, S.H., Bradley, J.R., and Wikle, C.K. (2019). “Spatio-Temporal Change of Support Modeling for the American Community Survey with R,” URL: https://arxiv.org/abs/1904.12092.
Sellers, K., Lotze, T., and Raim, A. (2019). COMPoissonReg: “Conway-Maxwell-Poisson Regression, version 0.7.0,” https://cran.r-project.org/package=COMPoissonReg
Sellers, K.F. and Young, D. (2019). “Zero-inflated Sum of Conway-Maxwell-Poissons (ZISCMP) Regression with Application to Shark Distributions,” Journal of Statistical Computation and Simulation, 89 (9): 1649-1673.
Sellers, K., Morris, D., Balakrishnan, N., and Davenport, D. (2018). multicmp: “Flexible Modeling of Multivariate Count Data via the Multivariate Conway-Maxwell-Poisson Distribution,” version 1.1, https://cran.r-project.org/package=multicmp
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., Holan, S.H., Bradley, J.R., and Wikle, C.K. (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.F., and Morris, D. (2017). “Under-dispersion Models: Models That Are ‘Under The Radar’,” Communications in Statistics – Theory and Methods, 46 (24): 12075-12086.
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.
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.
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.
Mathew, T., Menon, S., Perevozskaya, I., and Weerahandi, S. (2016). “Improved Prediction Intervals in Heteroscedastic Mixed-Effects Models,” Statistics & Probability Letters, 114, 48-53.
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.
Sellers, K.F., Morris, D.S., and Balakrishnan, N. (2016). “Bivariate Conway-Maxwell-Poisson Distribution: Formulation, Properties, and Inference,” Journal of Multivariate Analysis, 150:152-168.
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.
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.
Young, D. and Mathew, T. (2015). “Ratio Edits Based on Statistical Tolerance Intervals,” Journal of Official Statistics 31, 77-100.
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
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
Gamage, G., Mathew, T., and Weerahandi, S. (2013). “Generalized Prediction Intervals for BLUPs in Mixed Models,” Journal of Multivariate Analysis, 120, 226-233.
Mathew, T. and Young, D. S. (2013). “Fiducial-Based Tolerance Intervals for Some Discrete Distributions,” Computational Statistics and Data Analysis, 61, 38-49.
Young, D.S. (2013). “Regression Tolerance Intervals,” Communications in Statistics – Simulation and Computation, 42(9), 2040-2055.
Andrew Raim, Thomas Mathew, Kimberly Sellers, Darcy Morris
0331 – Working Capital Fund / General Research Project
Various Decennial and Demographic Projects