[Barth, J., J. Tillinghast, and M.H. Mulry. 2012. “Treatment of Influential Values in the Annual Survey of Public Employment and Payroll.” In Proceedings of the 2012]Search in Google Scholar
[Research Conference of the Federal Committee on Statistical Methods. Office of Management and Budget. Washington, DC. Available at: https://fcsm.sites.usa.gov/files/2014/05/Barth_2012FCSM_III-D.pdf (accessed October 10, 2014) Beaumont, J.-F. 2004. Robust Estimation of a Finite Population Total in the Presence of Influential Units. Report for the Office for National Statistics, dated July 23, 2004. Office for National Statistics, Newport, U.K.]Search in Google Scholar
[Beaumont, J.-F., and A. Alavi. 2004. “Robust Generalized Regression Estimation.” Survey Methodology 30: 195-208.]Search in Google Scholar
[Black, J. 2001. “Changes in Sampling Units in Surveys of Businesses.” In Proceedings of the Federal Committee on Statistical Methods Research Conference. Office of Management and Budget. Washington, DC. Available at: http://www.fcsm.gov/files/2014/05/2001FCSM_Black.pdf (accessed October 20, 2014)]Search in Google Scholar
[Chambers, R.L. and R. Ren. 2004. “Outlier Robust Imputation of Survey Data.” In Proceedings of the American Statistical Association, Section on Survey Research Methods [CD-ROM]. American Statistical Association. Alexandria, VA. 3336-3344. Available at: http://www.amstat.org/sections/SRMS/Proceedings/y2004/files/Jsm2004-000559.pdf (accessed October 20, 2014) Chambers, R.L., P. Kokic, P. Smith, and M. ]Search in Google Scholar
[Cruddas. 2000. “Winsorization for Identifying and Treating Outliers in Business Surveys.” In Proceedings of the Second International Conference on Establishment Surveys. Statistics Canada. Ottawa, Canada. 717-726.]Search in Google Scholar
[Clark, R. 1995. “Winsorization Methods in Sample Surveys.” Masters Thesis. Department of Statistics. Australia National University. Available at: http://hdl.handle.net/10440/1031 (accessed October 21, 2014) ]Search in Google Scholar
[Farrell, P.J. and M. Salibian-Barrera. 2006. “A Comparison of Several Robust Estimators for a Finite Population Mean.” Journal of Statistical Studies 26: 29-43.]Search in Google Scholar
[Hampel, F.R., E.M. Ronchetti, P.J. Rousseeuw, and S.A. Werner. 1986. Robust Statistics. An Approach Based on Influence Functions. New York: John Wiley & Sons.]Search in Google Scholar
[Huang, E. 1984. “An Imputation Study for the Monthly Retail Trade Survey.” In Proceedings Joint Statistical Meeting, Survey Research Methods Section, American Statistical Association. Alexandria, VA. 610-615.]Search in Google Scholar
[Huber, P.J. 1964. “Robust Estimation of a location parameter.” Annals of Mathematical Statistics. Institute of Mathematical Statistics 35: 73-101.10.1214/aoms/1177703732]Search in Google Scholar
[Hidiroglou, M.A. and J.-M. Berthelot. 1986. “Statistical Editing and Imputation for Periodic Business Surveys.” Survey Methodology 12: 73-83.]Search in Google Scholar
[Hulliger, B. 1995. “Outlier Robust Horvitz-Thompson Estimators.” Survey Methodology 21: 79-81.]Search in Google Scholar
[Hunt, J.W., J.S. Johnson, and C.S. King. 1999. “Detecting Outliers in the Monthly Retail Trade Survey Using the Hidiroglou-Berthelot Method.” In Proceedings of the Section on Survey Research Methods. American Statistical Association. Alexandria, VA. 539-543. Available at: http://www.amstat.org/sections/SRMS/Proceedings/papers/1999_093.pdf (accessed October 20, 2014)]Search in Google Scholar
[Kokic, P.N. and P.A. Bell. 1994. “Optimal Winsorising Cut-Offs for a Stratified Finite Population Estimator.” Journal of Official Statistics 10: 419-435.]Search in Google Scholar
[Lewis, D. 2007. “Winsorisation for estimates of change.” Proceedings of the Third International Conference on Establishment Surveys. American Statistical Association. Alexandria, VA. 1165-1172.]Search in Google Scholar
[Mulry, M.H. and B. Oliver. 2009. “A Simulation Study of Treatments of Influential Values in the Monthly Retail Trade Survey.” JSM Proceedings, Survey Research Methods Section. American Statistical Association. Alexandria, VA. 2979-2993. Available at: http://www.amstat.org/sections/SRMS/Proceedings/y2009/Files/304284.pdf (accessed October 20, 2014)]Search in Google Scholar
[Mulry, M.H. and R. Feldpausch. 2007a. “Investigation of Treatment of Influential Values.” Proceedings of the Third International Conference on Establishment Surveys. American Statistical Association. Alexandria, VA. 1173-1179.]Search in Google Scholar
[Mulry, M.H. and R. Feldpausch. 2007b. “Treating Influential Values in a Monthly Retail Trade Survey.” Proceedings of the Survey Methods Section, SSC Annual Meeting. Statistical Society of Canada. Ottawa, Ontario, Canada. Available at: http://www.ssc.ca/survey/documents/SSC2007_M_Mulry.pdf (accessed October 20, 2014)]Search in Google Scholar
[Ren, R. and R.L. Chambers. 2003. “Outlier Robust Imputation of Survey Data via Reverse Calibration.” S3RI Methodology Working Paper M03/19. Southampton Statistical Sciences Research Institute, University of Southampton, U.K. Available at: http://www.eprints.soton.ac.uk/8169/1/8169-01.pdf (accessed October 20, 2014)]Search in Google Scholar
[Rousseeuw, P.J. 1984. “Least Median of Squares Regression.” Journal of the American Statistical Association 79: 871-880.10.1080/01621459.1984.10477105]Search in Google Scholar
[Rousseeuw, P.J. and A.M. Leroy. 1987. Robust Regression and Outlier Detection. New York: John Wiley & Sons.10.1002/0471725382]Search in Google Scholar
[Särndal, C.-E., B. Swensson, and J. Wretman. 1992. Model Assisted Survey Sampling. New York: Springer-Verlag.10.1007/978-1-4612-4378-6]Search in Google Scholar
[Thompson, J.R. 2000. Simulation: A Modeler’s Approach. New York: John Wiley and Sons. 87-110.]Search in Google Scholar
[Thompson, K.J. and K.T. Washington. 2013. “Challenges in the Treatment of Unit Nonresponse for Selected Business Surveys: A Case Study.” Survey Methods: Insights from the Field. Available at: http://surveyinsights.org/?p¼2991 (accessed October 20, 2014)]Search in Google Scholar
[Thompson, K.J. and R.S. Sigman. 1999. “Statistical Methods for Developing Ratio Edit Tolerances for Economic Data.” Journal Official Statistics 15: 517-535. ]Search in Google Scholar