[Abraham, K.G., A. Mailand, and S.M. Bianchi. 2006. “Nonresponse in the American Time Use Survey. Who is Missing from the Data and How Much Does it Matter?” Public Opinion Quarterly 70: 676-703. DOI: http://dx.doi.org/10.1093/poq/nfl037.10.1093/poq/nfl037]Search in Google Scholar
[Axinn, W., C. Link, and R. Groves. 2011. “Responsive Survey Design, Demographic Data Collection, and Models of Demographic Behavior.” Demography 48: 1127-1149. DOI: http://dx.doi.org/10.1007/s13524-011-0044-1.10.1007/s13524-011-0044-121706256]Search in Google Scholar
[Banfield, E., L.O. Hall, K.W. Bowyer, and W.P. Kegelmeyer. 2007. “A Comparison of Decision Tree Ensemble Creation Techniques.” IEEE Transactions on Pattern Analysis and Machine Intelligence 29: 173-180. DOI: http://dx.doi.org/10.1109/TPAMI.2007.250609.10.1109/TPAMI.2007.25060917108393]Search in Google Scholar
[Bauer, E. and R. Kohavi. 1999. “An Emperical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants.” Machine Learning 36: 105-132. DOI: http://dx.doi.org/10.1023/A:1007515423169.10.1023/A:1007515423169]Search in Google Scholar
[Breiman, L. 1998. “Arcing Classifiers (with discussion).” Annals of Statistics 26: 801-849.10.1214/aos/1024691079]Search in Google Scholar
[Brick, J.M. and D. Williams. 2009. “Reasons for Increasing Nonresponse in U.S. Household Surveys.” Paper presented at the Workshop of the Committee on National Statistics, Washington, DC, December 14.]Search in Google Scholar
[Curtin, R., S. Presser, and E. Singer. 2005. “Changes in Telephone Survey Nonresponse over the Last Quarter Century.” Public Opinion Quarterly 69: 87-98. DOI: http://dx.doi.org/10.1093/poq/nfi002.10.1093/poq/nfi002]Search in Google Scholar
[Dietterich, T.G. 2000. “Ensemble Methods in Machine Learning.” In Proceedings of the Multiple Classifier Systems: First International Workshop, MCS 2000, June 21-23, Cagliari, Italy. Available at: http://www.eecs.wsu.edu/,holder/courses/CptS570/fall07/papers/Dietterich00.pdf (accessed August 2014).]Search in Google Scholar
[Dillman, D. 1978. Mail and Telephone Surveys: The Total Design Method. New York: Wiley & Sons.]Search in Google Scholar
[Earp, M., J. McCarthy, E. Porter, and P. Kott. 2010. “Assessing the Effect of Calibration on Nonresponse Bias in the 2008 ARMS Phase III Sample Using Census 2007 Data.” In Proceedings of the Joint Statistical Meetings: American Statistical Association. Alexandria, VA: American Statistical Association. Available at: http://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/conferences/JSM-2010/earp-2010_jsm_paper_arms_calibration.pdf (accessed August 2014).]Search in Google Scholar
[Eltinge, J.L. and I.S. Yansaneh. 1997. “Diagnostics for Formation of Nonresponse Adjustment Cells, with an Application to Income Nonresponse in the US Consumer Expenditure Survey.” Survey Methodology 23: 33-40.]Search in Google Scholar
[Groves, R. 2006. “Nonresponse Rates and the Nonresponse Bias in Household Surveys.” Public Opinion Quarterly 70: 646-675. DOI: http://dx.doi.org/10.1093/poq/nfl033.10.1093/poq/nfl033]Search in Google Scholar
[Groves, R. and M. Couper. 1998. Nonresponse in Household Interview Surveys. New York: Wiley.10.1002/9781118490082]Search in Google Scholar
[Groves, R.M., D. Dillman, J.L. Eltinge, and R.J. Little. 2002. Survey Nonresponse. New York: Wiley.]Search in Google Scholar
[Groves, R. and S. Heeringa. 2006. “Responsive Design for Household Surveys: Tools for Actively Controlling Survey Errors and Costs.” Journal of the Royal Statistical Society Series A: Statistics in Society 169: 439-457. DOI: http://dx.doi.org/10.1111/j.1467-985X.2006.00423.x.10.1111/j.1467-985X.2006.00423.x]Search in Google Scholar
[Johansson, F. and A. Klevmarken. 2008. “Explaining the Size and Nature of Response in a Survey on Health Status and Economic Standard.” Journal of Official Statistics 24: 431-449.]Search in Google Scholar
[Johnson, T.P., I.K. Cho, R.T. Campbell, and A.L. Holbrook. 2006. “Using Community- Level Correlates to Evaluate Nonresponse Effects in a Telephone Survey.” Public Opinion Quarterly 70: 704-719. DOI: http://dx.doi.org/10.1093/poq/nfl032.10.1093/poq/nfl032]Search in Google Scholar
[Kalton, G. and I. Flores-Cervantes. 2003. “Weighting Methods.” Journal of Official Statistics 19: 81-97.]Search in Google Scholar
[Laflamme, F. and M. Karaganis. 2010. “Development and Implementation of Responsive Design for CATI Surveys at Statistics Canada.” In Proceedings of the European Quality Conference: Helsinki, Finland.]Search in Google Scholar
[Lepkowsi, J.M. and M.P. Couper. 2002. “Nonresponse in the Second Wave of Longitudinal Household Surveys.” In Survey Nonresponse, edited by R.M. Groves, D.A. Dillman, J.L. Eltinge, and R.J.A. Little. New York: Wiley and Sons.]Search in Google Scholar
[Little, J. and D. Rubin. 2002. Statistical Analysis with Missing Data. New York: Wiley.10.1002/9781119013563]Search in Google Scholar
[Little, R. 1986. “Survey Nonresponse Adjustments for Estimates of Means.” Journal of the American Statistical Association 77: 237-250.10.1080/01621459.1982.10477792]Search in Google Scholar
[Little, R. and S. Vartivarian. 2005. “Does Weighting for Nonresponse Increase the Variance of Survey Means?” Survey Methodology 31: 161-168.]Search in Google Scholar
[Luzi, O., T. De Waal, B. Hulliger, M. Di Zio, J. Pannekoek, D. Kilchmann, and C. Tempelman. 2007. Recommended Practices for Editing and Imputation in Crosssectional Business Surveys. Italian Statistical Institute ISTAT. Matignon, R. 2008. Data Mining Using SAS Enterprise Miner. Cary, NC: SAS Institute Inc.]Search in Google Scholar
[McFadden, D. 1974. “Conditional Logit Analysis of Qualitative Choice Behavior.” In Frontiers in Econometrics, edited by P. Zarembka. New York: Academic Press.]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 Joint Statistical Meetings: American Statistical Association.]Search in Google Scholar
[Alexandria, VA: American Statistical Association. Available at: https://www.amstat.org/sections/srms/proceedings/y2010/Files/306438_56491.pdf (accessed August 2014).]Search in Google Scholar
[Mohl, C. and F. Laflamme. 2007. “Research and Responsive Design Options for Survey Data Collection at Statistics Canada.” In Proceedings of the Joint Statistical Meetings: American Statistical Association. Alexandria, VA: American Statistical Association. Available at: https://www.amstat.org/sections/srms/proceedings/y2007/Files/JSM2007-000421.pdf (accessed August 2014).]Search in Google Scholar
[Neville, P. 1999. Decision Trees for Predictive Modeling. Carey, NC: SAS Institute, Inc.]Search in Google Scholar
[Nicoletti, C. and F. Peracchi. 2005. “Survey Response and Survey Characteristics: Microlevel Evidence from the European Community Household Panel.” Journal of the Royal Statistical Society Series A: 168: 763-781. DOI: http://dx.doi.org/10.1111/j.1467-985X.2005.00369.x.10.1111/j.1467-985X.2005.00369.x]Search in Google Scholar
[Petroni, R., R. Sigman, D. Willimack, S. Cohen, and C. Tucker. 2004. “Response Rates and Nonresponse in Establishment Surveys - BLS and Census Bureau.” Federal Economic Statistics Advisory Committee, 1-50.]Search in Google Scholar
[Phipps, P. and D. Toth. 2012. “Analyzing Establishment Nonresponse Using an Interpretable Regression Tree Model with Linked Administrative Data.” Annals of Applied Statistics 6: 772-794. DOI: http://dx.doi.org/10.1214/11-AOAS521.10.1214/11-AOAS521]Search in Google Scholar
[Powers, R., J. Eltinge, and M. Cho. 2006. “Evaluation of the Detectability and Inferential Impact of Nonresponse Bias in Establishment Surveys.” In Proceedings of the Joint Statistical Meetings: American Statistical Association. Alexandria, VA: American Statistical Association. Available at: http://www.bls.gov/ore/pdf/st060130.pdf (accessed August 2014).]Search in Google Scholar
[Rosenbaum, P. and D. Rubin. 1983. “The Central Role of the Propensity Score in Observational Studies for Causal Effects.” Biometrika 70: 41-55. DOI: http://dx.doi.org/10.1093/biomet/70.1.41.10.1093/biomet/70.1.41]Search in Google Scholar
[Särndal, C.-E. 2011. “The 2010 Morris Hansen Lecture Dealing with Survey Nonresponse in Data Collection, in Estimation.” Journal of Official Statistics 27: 1-21. SAS Institute Inc. Enterprise Miner 6.2 Help and Documentation. Cary, NC: SAS Institute Inc., 2009.]Search in Google Scholar
[Schouten, B. 2007. “A Selection Strategy for Weighting Variables Under a Not-Missingat- Random Assumption.” Journal of Official Statistics 23: 51-68.]Search in Google Scholar
[Schouten, B. and G. de Nooij. 2005. Nonresponse Adjustment Using Classification Trees. CBS, Statistics Netherlands. Available at: http://www.cbs.nl/NR/rdonlyres/1245916E-80D5-40EB-B047-CC45E728B2A3/0/200501x10pub.pdf (accessed August 2014).]Search in Google Scholar
[Stussman, B., J. Dahlhamer, and C. Simile. 2005. “The Effect of Interviewer Strategies on Contact and Cooperation Rates in the National Health Interview Survey.” Federal Committee on Statistical Methodology, Washington, DC Thompson, K.J. 2009. “Conducting Nonresponse Bias Analysis for Two Business Surveys at the US Census Bureau: Methods and (Some) Results.” In Proceedings of the Section on Survey Research Methods: American Statistical Association Alexandria, VA: American Statistical Association. Available at: http://www.scs.gmu.edu/,wss/wss100922linebackpaper.pdf (accessed August 2014).]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. Retrieved from http://surveyinsights.org/?p¼2991.]Search in Google Scholar
[United States Department of Agriculture. 2012. 2012 Agricultural Resource Management Survey - Phase III Cost and Returns Report Survey Administration Manual. Washington, DC: US Department of Agriculture.]Search in Google Scholar
[United States Department of Agriculture. 2007. 2007 Census of Agriculture. Washington, DC: US Department of Agriculture. Available at: http://www.agCensus.usda.gov/ Publications/2007/Full_Report/ (accessed August 2014).]Search in Google Scholar
[United States Executive Office of the President. 2006. Office of Management and Budget Standards and Guidelines for Statistical Surveys. Washington, DC: U.S. Executive Office of the President. Available at: http://www.whitehouse.gov/sites/default/files/omb/inforeg/statpolicy/standards_stat_surveys.pdf (accessed August 2014).]Search in Google Scholar
[Uther, W.T.B. and M.M. Veloso. 1998. “Tree Based Discretization for Continuous State Space Reinforcement Learning.” In Proceedings of AAAI-98, the Fifteenth National Conference on Artificial Intelligence: 769-774. Available at: http://www.cs.cmu.edu/,mmv/papers/will-aaai98.pdf (accessed August 2014).]Search in Google Scholar
[Wagner, J. 2012. “A Comparison of Alternative Indicators for the Risk of Nonresponse Bias.” Public Opinion Quarterly 76: 555-575. DOI: http://dx.doi.org/10.1093/poq/nfs032. 10.1093/poq/nfs032627697530538342]Search in Google Scholar