Regression Modeling and File Matching Using Possibly Erroneous Matching Variables

References

• Albert, J. (2014), “LearnBayes: Functions for Learning Bayesian Inference,” R package version 2.15.
   Google Scholar
• Bohensky, M. (2016), “Bias in Data Linkage Studies,” in Methodological Developments in Data Linkage, Chichester, United Kingdom: Wiley, pp. 63–82.
   Google Scholar
• Chambers, R. (2009), “Regression Analysis of Probability-Linked Data,” Statisphere, Official Statistics Research Series, Statistics New Zealand, 4, 1–72.
   Google Scholar
• Dunson, D. B., and Xing, C. (2009), “Nonparametric Bayes Modeling of Multivariate Categorical Data,” Journal of the American Statistical Association, 104, 1042–1051.
   Web of Science ®Google Scholar
• Fortini, M., Liseo, B., Nuccitelli, A., and Scanu, M. (2001), “On Bayesian Record Linkage,” Research in Official Statistics, 4, 185–198.
   Google Scholar
• Genz, A., Bretz, F., Miwa, T., Mi, X., Leisch, F., Scheipl, F., and Hothorn, T. (2017), “mvtnorm: Multivariate Normal and t Distributions”, R package version 1.0-6.
   Google Scholar
• Gutman, R., Afendulis, C., and Zaslavsky, A. (2013), “A Bayesian Procedure for File Linking to Analyze End-of-Life Medical Costs,” Journal of the American Statistical Association, 108, 34–47.
   PubMed Web of Science ®Google Scholar
• Herzog, T., Scheuren, F., and Winkler, W. (2007), Data Quality and Record Linkage Techniques, New York: Springer.
   Google Scholar
• Ishwaran, H., and James, L. F. (2001), “Gibbs Sampling Methods for Stick-Breaking Priors,” Journal of the American Statistical Association, 96, 161–173.
   Web of Science ®Google Scholar
• Jaro, M. A. (1989), “Advances in Record-Linkage Methodology as Applied to Matching the 1985 Census of Tampa, Florida,” Journal of the American Statistical Association, 84, 414–420.
   Web of Science ®Google Scholar
• Kim, H. J., Cox, L. H., Karr, A. F., Reiter, J. P., and Wang, Q. (2015), “Simultaneous Editing and Imputation for Continuous Data,” Journal of the American Statistical Association, 110, 987–999.
   Web of Science ®Google Scholar
• Lahiri, P., and Larsen, M. (2005), “Regression Analysis with Linked Data,” Journal of the American Statistical Association, 100, 222–230.
   Web of Science ®Google Scholar
• Larsen, M. (2004), “Record Linkage Using Finite Mixture Models,” in Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, eds. A. Gelman and X. Meng, Chichester, UK: Wiley, pp. 309–318.
   Google Scholar
• Larsen, M., and Rubin, D. (2001), “Iterative Automated Record Linkage Using Mixture Models,” Journal of the American Statistical Association, 96, 32–41.
   Web of Science ®Google Scholar
• Lemon, J. (2006), “Plotrix: A Package in the Red Light District of R,” R-News, 6, 8–12.
   Google Scholar
• Manrique-Vallier, D., and Reiter, J. P. (2017), “Bayesian Simultaneous Edit and Imputation for Multivariate Categorical Data,” Journal of the American Statistical Association, 12, 1708–1719.
   Google Scholar
• NCERDC (2013), “North Carolina Education Research Data Center: Duke Center for Child and Family Policy,” https://childandfamilypolicy.duke.edu/pdfs/projects/NCERDC_DataHoused.pdf.
   Google Scholar
• Neter, J., Maynes, E., and Ramanthan, R. (1965), “The Effect of Mismatching on the Measurement of Response Errors,” Journal of the American Statistical Association, 60, 1005–1027.
   Google Scholar
• R Core Team (2017a), “foreign: Read Data Stored by ’Minitab’, ’S’, ’SAS’, ’SPSS’, ’Stata’, ’Systat’, ’Weka’, ’dBase’, ...”, R package version 0.8-69.
   Google Scholar
• ——— (2017b), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
   Google Scholar
• Rubin, D. B. (1976), “Inference and Missing Data,” Biometrika, 63, 581–592.
   Web of Science ®Google Scholar
• Sadinle, M. (2016), “Bayesian Estimation of Bipartite Matchings for Record Linkage,” Journal of the American Statistical Association, forthcoming.
   Web of Science ®Google Scholar
• Samart, K., and Chambers, R. (2014), “Linear Regression With Nested Errors Using Probability-Linked Data,” Australian & New Zealand Journal of Statistics, 56, 27–46.
   Web of Science ®Google Scholar
• Scheuren, F., and Winkler, W. (1993), “Regression Analysis of Data Files that are Computer Matched,” Survey Methodology, 19, 39–58.
   Google Scholar
• ——— (1997), “Regression Analysis of Data Files that are Computer Matched, Part II,” Survey Methodology, 23, 157–165.
   Google Scholar
• Si, Y., and Reiter, J. P. (2013), “Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys,” Journal of Educational and Behavioral Statistics, 38, 499–521.
   Web of Science ®Google Scholar
• Simpson, G. L. (2016), “permute: Functions for Generating Restricted Permutations of Data,” R package version 0.9-4.
   Google Scholar
• Steorts, R. C., Hall, R., and Fienberg, S. E. (2014), “SMERED: A Bayesian Approach to Graphical Record Linkage and De-Duplication,” Journal of Machine Learning Research, 33, 922–930.
   Google Scholar
• Tancredi, A., and Liseo, B. (2011), “A Hierarchical Bayesian Approach to Record Linkage and Population Size Problems,” The Annals of Applied Statistics, 5, 1553–1585.
   Web of Science ®Google Scholar
• UCLA: Stat Consulting Group (2016), “hbsdemo.dta,” [Data File], available at http://www.ats.ucla.edu/stat/data/hsbdemo.dta.
   Google Scholar
• Venables, W. N., and Ripley, B. D. (2002), Modern Applied Statistics with S,(4th ed.) New York: Springer, ISBN 0-387-95457-0.
   Google Scholar
• Vermunt, J. K., Van Ginkel, J. R., Van Der Ark, L. A., and Sijtsma, K. (2008), “Multiple Imputation of Incomplete Categorical Data Using Latent Class Analysis,” Sociological Methodology, 38, 369–397.
   Web of Science ®Google Scholar
• Wang, Q., Manrique-Vallier, D., Reiter, J. P., and Hu, J. (2016), “NPBayesImpute: Non-Parametric Bayesian Multiple Imputation for Categorical Data”, R package version 0.6.
   Google Scholar
• Wickham, H. (2009), ggplot2: Elegant Graphics for Data Analysis, New York: Springer-Verlag.
   Google Scholar
• Winkler, W. (2004), “Methods for Evaluating and Creating Data Quality,” Information Systems, 29, 531–550.
   Web of Science ®Google Scholar
• Wu, Y. (1995), “Random Shuffling: A New Approach to Matching Problem,” in ASA Proceedings of the Statistical Computing Section, American Statistical Association, pp. 69–74.
   Google Scholar