The Utility of Nonparametric Transformations for Imputation of Survey Data

Azzalini, A. 1985. “A Class of Distributions Which Includes the Normal Ones.” Scandinavian Journal of Statistics 12: 171-178.Search in Google Scholar

Dempster, A.P., N.M. Laird, and D.B. Rubin. 1977. “Maximum Likelihood From Incomplete Data via the EM Algorithm (with discussion).” Journal of the Royal Statistical Society Series B 39: 1-38.Search in Google Scholar

Fay, R.E. 1996. “Alternative Paradigms for the Analysis of Imputed Survey Data.” Journal of the American Statistical Association 91: 490-498. DOI: http://dx.doi.org/10.1080/01621459.1996.10476909.10.1080/01621459.1996.10476909Search in Google Scholar

Huffman, W.E. 1980. “Farm and Off-Farm Work Decisions: The Role of Human Capital.” Review of Economics and Statistics 62: 14-23.10.2307/1924268Search in Google Scholar

Huffman, W.E., and M.D. Lange. 1989. “Off-Farm Work Decisions of Husbands and Wives: Joint Decision Making.” The Review of Economics and Statistics 71: 471-480. DOI: http://dx.doi.org/10.2307/1926904.10.2307/1926904Search in Google Scholar

Javaras, K.N., and D.A. van Dyk. 2003. “Multiple Imputation for Incomplete Data with Semicontinuous Variables.” Journal of the American Statistical Association 98: 703-715. DOI: http://dx.doi.org/10.1198/016214503000000611.10.1198/016214503000000611Search in Google Scholar

Kim, J.K., J.M. Brick, W.A. Fuller, and G. Kalton. 2006. “On the Bias of the Multiple- Imputation Variance Estimator in Survey Sampling.” Journal of the Royal Statistical Society Series B 68: 509-521. DOI: http://dx.doi.org/10.1111/j.1467-9868.2006.00546.x.10.1111/j.1467-9868.2006.00546.xSearch in Google Scholar

Kott, P.S. 1995. A Paradox of Multiple Imputation. Tech. rep., National Agricultural Statistics Service, Fairfax, VA. Presented at the Joint Statistical Meetings, August 1995, Orlando, FL Kott, P.S., and T. Chang. 2010. “Using Calibration Weighting to Adjust for Nonignorable Unit Nonresponse.” Journal of the American Statistical Association 105: 1265-1275. DOI: http://dx.doi.org/10.1198/jasa.2010.tm09016.10.1198/jasa.2010.tm09016Search in Google Scholar

Kwon, C.-W., P. Orazem, and D.M. Otto. 2006. “Off-Farm Labor Supply Responses to Permanent and Transitory Farm Income.” Agricultural Economics 34: 59-67. DOI: http://dx.doi.org/10.1111/j.1574-0862.2006.00103.x.10.1111/j.1574-0862.2006.00103.xSearch in Google Scholar

Little, R.J.A. 1988. “Missing-Data Adjustments in Large Surveys.” Journal of Business & Economic Statistics 6: 287-296. DOI: http://dx.doi.org/10.1080/07350015.1988.10509663.10.1080/07350015.1988.10509663Search in Google Scholar

Little, R.J.A., and D.B. Rubin. 2002. Statistical Analysis with Missing Data. Hoboken, NJ: John Wiley & Sons.10.1002/9781119013563Search in Google Scholar

Manrique-Vallier, D., and J.P. Reiter. 2014. “Bayesian Multiple Imputation for Large- Scale Categorical Data With Structural Zeros.” Survey Methodology 40: 125-134.Search in Google Scholar

Miller, D., M. Robbins, and J. Habiger. 2010. “Examining the Challenges of Missing Data Analysis in Phase Three of the Agricultural Resource Management Survey.” In Proceedings of the JSM, Section on Survey Research Methods: American Statistical Association. Alexandria, VA, 816-823.Search in Google Scholar

Mishra, A.K., and D.M. Holthausen. 2002. “Effect of Farm Income and Off-Farm Wage Variability on Off-Farm Labor Supply.” Agricultural and Resource Economics Review 31: 187-199.10.1017/S1068280500003993Search in Google Scholar

National Research Council. 2008. Understanding American Agriculture: Challenges for the Agricultural Resource Management Survey. Washington, D.C.: The National Academies Press.Search in Google Scholar

Nelsen, R.B. 2009. An introduction to Copulas. New York: Springer.Search in Google Scholar

Raghunathan, T., J. Lepkowski, J. van Hoewyk, and P. Solenberger. 2001. “A Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models.” Survey Methodology 27: 85-95.Search in Google Scholar

Raghunathan, T.E., P.W. Solenberger, and J. van Hoewyk. 2002. Iveware: Imputation and Variance Estimation Software. Ann Arbor, MI: Survey Methodology Program, Survey Research Center, Institute for Social Research, University of Michigan.Search in Google Scholar

Raghunathan, T., J. Reiter, and D. Rubin. 2003. “Multiple Imputation for Statistical Disclosure Limitation.” Journal of Official Statistics 19: 1-16.Search in Google Scholar

Reiter, J.P. 2002. “Satisfying Disclosure Restrictions With Synthetic Data Sets.” Journal of Official Statistics 18: 531-544.Search in Google Scholar

Reiter, J.P. 2005. “Releasing Multiply Imputed, Synthetic Public Use Microdata: An Illustration and Empirical Study.” Journal of the Royal Statistical Society Series A 168: 185-205. DOI: http://dx.doi.org/10.1111/j.1467-985X.2004.00343.x.10.1111/j.1467-985X.2004.00343.xSearch in Google Scholar

Robbins, M.W., S.K. Ghosh, B. Goodwin, J.D. Habiger, D. Miller, and T.K. White. 2011. Multivariate Imputation Methods for Addressing Missing Data in the Agricultural Resource Management Survey (ARMS). A NISS/NASS collaborative research project, National Agricultural Statistics Service/National Institute of Statistical Sciences.Search in Google Scholar

Robbins, M.W., and T.K. White. 2011. “Farm Commodity Payments and Imputation in the Agricultural Resource Management Survey.” American Journal of Agricultural Economics 93: 606-612. DOI: http://dx.doi.org/10.1093/ajae/aaq166.10.1093/ajae/aaq166Search in Google Scholar

Robbins, M.W., S.K. Ghosh, and J.D. Habiger. 2013. “Imputation in High-Dimensional Economic Data as Applied to the Agricultural Resource Management Survey.” Journal of the American Statistical Association 108: 81-95. DOI: http://dx.doi.org/10.1080/01621459.2012.734158.10.1080/01621459.2012.734158Search in Google Scholar

Robbins, M.W., and T.K. White. Forthcoming. “Direct Payments, Cash Rents, Land Values, and the Effects of Imputation in U.S. Farm-Level Data.” Agricultural and Resource Economics Review.Search in Google Scholar

Rubin, D.B. 1987. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley & Sons.10.1002/9780470316696Search in Google Scholar

Rubin, D.B. 1993. “Discussion of Statistical Disclosure Limitation.” Journal of Official Statistics 9: 461-468.Search in Google Scholar

Rubin, D.B. 1996. “Multiple Imputation After 18 þ Years.” Journal of the American Statistical Association 91: 473-489. DOI: http://dx.doi.org/10.1080/01621459.1996.10476908.10.1080/01621459.1996.10476908Search in Google Scholar

Schafer, J.L. 1997. Analysis of Incomplete Multivariate Data. New York: Chapman and Hall/CRC.10.1201/9781439821862Search in Google Scholar

Scott, D.W. 2009. Multivariate Density Estimation: Theory, Practice, and Visualization. Vol. 383. New York: Wiley.Search in Google Scholar

Sheather, S.J., and M.C. Jones. 1991. “A Reliable Data-Based Bandwidth Selection Method for Kernel Density Estimation.” Journal of the Royal Statistical Society Series B 53: 683-690.10.1111/j.2517-6161.1991.tb01857.xSearch in Google Scholar

Silverman, B.W. 1986. Density Estimation for Statistics and Data Analysis. Vol. 26. New York: CRC Press.Search in Google Scholar

Su, Y.-S., M. Yajima, A.E. Gelman, and J. Hill. 2011. “Multiple Imputation with Diagnostics (mi) in r: Opening Windows into the Black Box.” Journal of Statistical Software 45: 1-31.10.18637/jss.v045.i02Search in Google Scholar

Sumner, D.A. 1982. “The Off-Farm Labor Supply of Farmers.” American Journal of Agricultural Economics 64: 499-509. DOI: http://dx.doi.org/10.2307/1240642.10.2307/1240642Search in Google Scholar

Templ, M., A. Kowarik, and P. Filzmoser. 2011. “Iterative Stepwise Regression Imputation Using Standard and Robust Methods.” Computational Statistics & Data Analysis 55: 2793-2806. DOI: http://dx.doi.org/10.1016/j.csda.2011.04.012.10.1016/j.csda.2011.04.012Search in Google Scholar

U.S. Department of Agriculture. 2011. Farm Production Expenditures 2010 Summary. Washington, D.C.Search in Google Scholar

Van Buuren, S., and C.G.M. Oudshoorn. 1999. Flexible Multivariate Imputation by0 MICE.Search in Google Scholar

Leiden: TNO Preventie en Gezondheid. For associated software see http://www.multiple-imputation.com (accessed October 21, 2014).Search in Google Scholar

Woodcock, S.D., and G. Benedetto. 2009. “Distribution-Preserving Statistical Disclosure Limitation.” Computational Statistics and Data Analysis 53: 4228-4242. DOI: http://dx.doi.org/10.1016/j.csda.2009.05.020. 10.1016/j.csda.2009.05.020Search in Google Scholar