Stop or Continue Data Collection: A Nonignorable Missing Data Approach for Continuous Variables

Andridge, R. and K.J. Thompson. 2015. “Assessing Nonresponse Bias in a Business Survey: Proxy Pattern-Mixture Analysis for Skewed Data.” Annals of Applied Statistics 9: 2237–2265. Doi: http://dx.doi.org/10.1214/15-AOAS878.10.1214/15-AOAS878Search in Google Scholar

Andridge, R.R. and R.J. Little. 2011. “Proxy Pattern-Mixture Analysis for Survey Nonresponse.” Journal of Official Statistics 27(2): 153. Available at: http://www.jos.nu/articles/abstract.asp?article=272153.Search in Google Scholar

Daniels, M. and J. Hogan. 2008. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis. Chapman & Hall/CRC Press. Taylor & Francis, Boca Raton.10.1201/9781420011180Search in Google Scholar

Diggle, P. and M.G. Kenward. 1994. “Informative Drop-Out in Longitudinal Data Analysis.” Journal of the Royal Statistical Society, 43(1): 49–93. Doi: http://dx.doi.org/10.2307/2986113.10.2307/2986113Search in Google Scholar

Dunson, D.B. 2010. “Nonparametric Bayes Applications to Biostatistics.” In Bayesian Nonparametrics, edited by N. Hjort, C. Holmes, P. Müller, and S. Walker. Cambridge Series in Statistical and Probabilistic Mathematics, 223–273. Cambridge University Press.10.1017/CBO9780511802478.008Search in Google Scholar

Dunson, D.B. and C. Xing. 2009. “Nonparametric Bayes Modeling of Multivariate Categorical Data.” Journal of the American Statistical Association 104(487): 1042–1051. Doi: http://dx.doi.org/10.1198/jasa.2009.tm08439.10.1198/jasa.2009.tm08439363037823606777Search in Google Scholar

Escobar, M.D. and M. West. 1995. “Bayesian Density Estimation and Inference Using Mixtures.” Journal of the American Statistical Association 90(430): 577–588. Doi: http://dx.doi.org/10.1080/01621459.1995.10476550.10.1080/01621459.1995.10476550Search in Google Scholar

Ferguson, T.S. 1973. “A Bayesian Analysis of Some Nonparametric Problems.” The Annals of Statistics 1(2): 209–230. Doi: http://dx.doi.org/10.1214/aos/1176342360.10.1214/aos/1176342360Search in Google Scholar

Ferguson, T.S. 1983. “Bayesian Density Estimation by Mixtures of Normal Distributions.” In Recent Advances in Statistics 24: 287–302. New York Academic Press.10.1016/B978-0-12-589320-6.50018-6Search in Google Scholar

Finamore, J., B. Reist, and S. Coffey. 2013. 2013 National Survey of College Graduates: A practice-based investigation of adaptive design. In AAPOR (American Association for Public Opinion Research) 68th Annual Conference, Boston, MA, May 16–19, 2013. Available at: https://www.aapor.org/Conference/Recent-Conferences/2013-Conference.aspx.Search in Google Scholar

Fraley, C. and A.E. Raftery. 2007. “Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering.” Journal of Classification 24(2): 155–181. Doi: http://dx.doi.org/10.1007/s00357-007-0004-5.10.1007/s00357-007-0004-5Search in Google Scholar

Ghosal, S. and A. van der Vaart. 2001. “Entropies and Rates of Convergence for Maximum Likelihood and Bayes Estimation for Mixtures of Normal Densities.” The Annals of Statistics 29(5): 1233–1263. Available at: http://www.jstor.org/stable/2699987.10.1214/aos/1013203452Search in Google Scholar

Ghosal, S. and A. van der Vaart. 2007. “Posterior Convergence Rates of Dirichlet Mixtures at Smooth Densities.” The Annals of Statistics 35(2): 697–723. Doi: http://dx.doi.org/10.1214/009053606000001271.10.1214/009053606000001271Search in Google Scholar

Glynn, R., N. Laird, and D. Rubin. 1986. “Selection Modeling Versus Mixture Modeling with Nonignorable Nonresponse.” In Drawing Inferences from Self-Selected Samples, edited by H. Wainer, 115–142. New York: Springer.10.1007/978-1-4612-4976-4_10Search in Google Scholar

Glynn, R.J., N.M. Laird, and D.B. Rubin. 1993. “Multiple Imputation in Mixture Models for Nonignorable Nonresponse with Follow-Ups.” Journal of the American Statistical Association 88(423): 984–993. Doi: http://dx.doi.org/10.2307/2290790.10.2307/2290790Search in Google Scholar

Groves, R. 2004. Survey Errors and Survey Costs. Wiley Series in Probability and Statistics. New York: Wiley.Search in Google Scholar

Ishwaran, H. and L.F. James. 2001. “Gibbs Sampling Methods for Stick-Breaking Priors.” Journal of the American Statistical Association 96(453). Doi: http://dx.doi.org/10.1198/016214501750332758.10.1198/016214501750332758Search in Google Scholar

Kaputa, S.J., L. Bechtel, K.J. Thompson, and D. Whitehead. 2014. “Strategies for Subsampling Nonrespondents for Economic Programs.” In JSM Proceedings, Survey Research Methods Section. American Statistical Association, Alexandria, VA, August 2–7, 2014. Available at: http://ww2.amstat.org/meetings/JSM/2014/onlineprogram/AbstractDetails.cfm?abstractid=311881.Search in Google Scholar

Kim, H.J., L.H. Cox, A.F. Karr, J.P. Reiter, and Q. Wang. 2015. “Simultaneous Edit-imputation for Continuous Microdata.” Journal of the American Statistical Association 110(511): 987–999. Doi: http://dx.doi.org/10.2139/ssrn.2698601.10.2139/ssrn.2698601Search in Google Scholar

Kim, H.J., J.P. Reiter, Q. Wang, L.H. Cox, and A.F. Karr. 2014. “Multiple Imputation of Missing or Faulty Values Under Linear Constraints.” Journal of Business and Economic Statistics 32(3): 375–386. Doi: http://dx.doi.org/{10.1080/07350015.2014.885435}.10.1080/07350015.2014.885435Search in Google Scholar

Little, R. 1993. “Pattern-Mixture Models for Multivariate Incomplete Data.” Journal of the American Statistical Association 88(421): 125–134. Available at: http://www.jstor.org/stable/2290705.10.1080/01621459.1993.10594302Search in Google Scholar

Little, R. 1994. “A Class of Pattern-Mixture Models for Normal Incomplete Data.” Biometrika 81(3): 471–483. Doi: http://dx.doi.org/10.1093/biomet/81.3.471.10.1093/biomet/81.3.471Search in Google Scholar

Little, R. 1995. “Modeling the Drop-Out Mechanism in Repeated-Measures Studies.” Journal of the American Statistical Association 90(431): 1112–1121. Doi: http://dx.doi.org/10.2307/2291350.10.2307/2291350Search in Google Scholar

Little, R. 2008. “Selection and Pattern-Mixture Models.” In Longitudinal Data Analysis, edited by G. Fitzmaurice, M. Davidian, G. Verbeke, and G. Molenberghs, 409–431. Chapman and Hall/CRC: Boca Raton.10.1201/9781420011579.ch18Search in Google Scholar

Little, R. and D. Rubin. 2002. Statistical Analysis with Missing Data. Wiley Series in Probability and Statistics. Wiley: Hoboken, NJ. Second edition.10.1002/9781119013563Search in Google Scholar

Miller, P.V. 2013. “Adaptive Design at the Census Bureau – a New Way of Doing Business.” In Proceedings of AAPOR (American Association for Public Opinion Research) 68th Annual Conference, 171–171, Boston, MA, May 16–19, 2013. Available at: https://www.aapor.org/Conference/Recent-Conferences/2013-Conference.aspx.Search in Google Scholar

Molenberghs, G., M.G. Kenward, and E. Lesaffre. 1997. “The Analysis of Longitudinal Ordinal Data with Nonrandom Drop-Out.” Biometrika 84(1): 33–44. Doi: http://dx.doi.org/10.1093/biomet/84.1.33.10.1093/biomet/84.1.33Search in Google Scholar

Molenberghs, G., B. Michiels, M.G. Kenward, and P.J. Diggle. 1998. “Monotone Missing Data and Pattern-Mixture Models.” Statistica Neerlandica 52(2): 153–161. Doi: http://dx.doi.org/10.1111/1467-9574.00075.10.1111/1467-9574.00075Search in Google Scholar

Müller, P. and R. Mitra. 2013. “Bayesian Nonparametric Inference – Why and How.” Bayesian Analysis 8(2): 269–302. Doi: http://dx.doi.org/10.1214/13-BA811.10.1214/13-BA811387016724368932Search in Google Scholar

Müller, P. and F.A. Quintana. 2004. “Nonparametric Bayesian Data Analysis.” Statistical Science 19(1): 95–110. Doi: http://dx.doi.org/10.1214/088342304000000017.10.1214/088342304000000017Search in Google Scholar

Murray, J.S. and J.P. Reiter. 2016. “Multiple Imputation of Missing Categorical and Continuous Values via Bayesian Mixture Models with Local Dependence.” Journal of the American Statistical Association 111(516): 1466–1479.10.1080/01621459.2016.1174132Search in Google Scholar

R Core Team. 2013. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Doi: http://dx.doi.org/10.1080/01621459.2016.1174132.10.1080/01621459.2016.1174132Search in Google Scholar

Rao, R.S., M.E. Glickman, and R.J. Glynn. 2008. “Stopping Rules for Surveys with Multiple Waves of Nonrespondent Follow-up.” Statistics in Medicine 27(12): 2196–2213. Doi: http://dx.doi.org/10.1002/sim.3063.10.1002/sim.306317886234Search in Google Scholar

RStudio and Inc. 2014. “shiny: Web Application Framework for R. R package version 0.9.1”.Search in Google Scholar

Rubin, D. 1987. Multiple Imputation for Nonresponse in Surveys. Wiley: Hoboken, NJ.10.1002/9780470316696Search in Google Scholar

Rubin, D.B. 1976. “Inference and Missing Data.” Biometrika 63(3): 581–592. Doi: http://dx.doi.org/10.2307/2335739.10.2307/2335739Search in Google Scholar

Schouten, B., J. Bethlehem, K. Beullens, Ø. Kleven, G. Loosveldt, A. Luiten, K. Rutar, N. Shlomo, and C. Skinner. 2012. “Evaluating, Comparing, Monitoring, and Improving Representativeness of Survey Response Through r-Indicators and Partial r-Indicators.” International Statistical Review 80(3): 382–399. Doi: http://dx.doi.org/10.1111/j.1751-5823.2012.00189.x.10.1111/j.1751-5823.2012.00189.xSearch in Google Scholar

Schouten, B., F. Cobben, and J. Bethlehem. 2009. “Indicators for the Representativeness of Survey Response.” Survey Methodology 35(1): 101–113.Search in Google Scholar

Schouten, B., N. Shlomo, and C. Skinner. 2011. “Indicators for Monitoring and Improving Representativeness of Response.” Journal of Official Statistics 27(2): 1–24.Search in Google Scholar

Sethuraman, J. 1994. “A Constructive Definition of Dirichlet Priors.” Statistica Sinica 4: 639–650. Available at: http://www.jstor.org/stable/24305538.Search in Google Scholar

Shen, W., S.T. Tokdar, and S. Ghosal. 2013. “Adaptive Bayesian Multivariate Density Estimation with Dirichlet Mixtures.” Biometrika 100(3): 623–640. Doi: http://dx.doi.org/10.1093/biomet/ast015.10.1093/biomet/ast015Search in Google Scholar

Thijs, H., G. Molenberghs, B. Michiels, G. Verbeke, and D. Curran. 2002. “Strategies to Fit Pattern-Mixture Models.” Biostatistics 3(2): 245–265. Doi: http://dx.doi.org/10.1007/978-1-4757-3625-0_27.10.1007/978-1-4757-3625-0_27Search in Google Scholar

U. S. Census Bureau. 2015. Annual Survey of Manufactures (ASM). Available at: https://www.census.gov/programs-surveys/asm.html (accessed June 30, 2017).Search in Google Scholar

Wagner, J. 2008. “Adaptive Survey Design to Reduce Nonresponse Bias.” PhD thesis, University of Michigan. Available at: http://hdl.handle.net/2027.42/60831.Search in Google Scholar

Wagner, J. and T.E. Raghunathan. 2010. “New Stopping Rule for Surveys.” Statistics in Medicine 29(9): 1014–1024. Doi: http://dx.doi.org/10.1002/sim.3834.10.1002/sim.383420131311Search in Google Scholar

West, M. and G. Cao. 1993. “Assessing Mechanisms of Neural Synaptic Activity.” In Case Studies in Bayesian Statistics, edited by Gatsonis, Constantine and Hodges, James S. and Kass, Robert E. and Singpurwalla, Nozer D. 416–428. Springer-Verlag: New York.10.1007/978-1-4612-2714-4_16Search in Google Scholar

West, M., P. Müller, and M.D. Escobar. 1994. “Hierarchical Priors and Mixture Models, with Application in Regression and Density Estimation.” In Aspects of Uncertainty: A Tribute to D.V. Lindley, edited by A. Smith and P. Freeman, 363–386. London: Wiley.Search in Google Scholar

Woo, M.-J., J.P. Reiter, and A.F. Karr. 2008. “Estimation of Propensity Scores Using Generalized Additive Models.” Statistics in Medicine 27(19): 3805–3816. Doi: http://dx.doi.org/10.1002/sim.3278.10.1002/sim.327818366144Search in Google Scholar

Woo, M.-J., J.P. Reiter, A. Oganian, and A.F. Karr. 2009. “Global Measures of Data Utility for Microdata Masked for Disclosure Limitation.” Journal of Privacy and Confidentiality 1(1): 7. Available at: http://repository.cmu.edu/jpc/vol1/iss1/7.10.29012/jpc.v1i1.568Search in Google Scholar