From predictive uplift modeling to prescriptive uplift analytics: A practical approach to treatment optimization while accounting for estimation risk
Tóm tắt
Từ khóa
Tài liệu tham khảo
Angrist, J.D. and Pischke, J.-S. (2014) Mastering Metrics: The Path from Cause to Effect, Princeton, NJ: Princeton University Press.
Banerjee, A., Bandyopadhyay, T. and Acharya, P. (2013) Data analytics: Hyped up aspirations or true potential? Vikapla 38 (4), http://www.vikalpa.com/pdf/articles/2013/04-Perspectives.pdf , accessed 20 April 2015.
Ben-Tal, A., El Ghaoui, L. and Nemirovski, A. (2009) Robust Optimization, Princeton, NJ: Princeton University Press.
Bertsimas, D. and Tsitsiklis, J.N. (1997) Introduction to Linear Programming, Belmont, MA: Athena Scientific.
Cai, T., Tian, L., Wong, P.H. and Wei, L.J. (2011) Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics 12 (2): 270–282.
Cornuejols, G. and Tutuncu, R. (2007) Optimization Methods in Finance, New York, NY: Cambridge.
Dasgupta, S., Papadimitriou, C.H. and Vazirani, U.V. (2006) Algorithms, New York, NY: McGraw-Hill.
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Applications, Cambridge, UK: Cambridge University Press.
Fabozzi, F.J., Kolm, P.N., Pachamanova, D.A. and Focardi, S.M. (2007) Robust Portfolio Optimization and Management, Hoboken, NJ: Wiley.
Goldberg, D.E. (1989) Genetic Algorithms in Search, Optimization & Machine Learning, Addison-Wesley.
Guelman, L., Guillen, M. and Perez-Marin, A.M. (2014) A survey of personalized treatment models for pricing strategies in insurance. Insurance: Mathematics and Economics 58: 68–76.
Hastie, T., Tibshirani, R. and Friedman, J. (2011) The Elements of Statistical Learning. 2nd edn. New York, NY: Springer.
Haughton, D., Haughton, J. and Lo, V.S.Y. (2016) Cause-and-Effect Business Analytics, CRC/Chapman & Hall.
Holland, C. (2005) Breakthrough Business Results with MVT, Hoboken, NJ: Wiley.
James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013) An Introduction to Statistical Learning: With Applications in R, New York, NY: Springer.
Kane, K., Lo, V.S.Y. and Jane, Z. (2014) Mining for the truly responsive customers and prospects using true-lift modeling: Comparison of new and existing methods. Journal of Marketing Analytics 2 (4): 218–238.
Kubiak, R. (2012) Net Lift Model for Effective Direct Marketing Campaigns at 1800flowers.com. SAS Global Forum, Paper 108-2012.
Ledolter, J. and Swersey, A.J. (2007) Testing 1–2–3: Experimental Design with Applications in Marketing and Service Operations, Stanford, CA: Stanford University Press.
Lo, V.S.Y. (2002) The true-lift model – A novel data mining approach to response modeling in database marketing. ACM SIGKDD Explorations 4 (2): 78–86.
Lo, V.S.Y. (2008) New opportunities in marketing data mining. In: J. Wang (ed.) Encyclopedia of Data Warehousing and Mining. 2nd edn. Idea Group Publishing.
Lund, B. (2012) Direct Marketing Profit Model. In: Proceedings of Midwest SAS Users Group, Paper CI-04.
Lustig, I., Dietrich, B., Johnson, C. and Dziekan, C. (2010) The analytics journey. Analytics Magazine, Nov/Dec: 11–18.
Manzi, J. (2012) Uncontrolled: The Surprising Payoff of Trial-and-Error for Business, Politics, and Society, Philadelphia, PA: Basic Books.
Markowitz, H. (1952) Portfolio selection. Journal of Finance VII (1): 77–91.
Michalewicz, Z. and Fogel, D.B. (2002) How to Solve It: Modern Heuristics, Berlin, Germany: Springer.
Nassif, H., Kuusisto, F., Burnside, E.S. and Shavlik, J. (2013) Uplift modeling with ROC: An SRL case study. Proceedings of the International Conference on Inductive Logic Programming (ILP’13), Rio de Janeiro, Brazil.
Papadimitriou, C.H. and Steiglitz, K. (1998) Combinational Optimization: Algorithms and Complexity, Mineola, NY: Dover.
Pisinger, D. (1995) Algorithms for knapsack problems. PhD dissertation, Department of Computer Science, University of Copenhagen.
Porter, D. (2013) Pinpointing the persuadables: Convincing the right voters to support Barack Obama. Presented at Predictive Analytics World; October, Boston, MA. http://www.predictiveanalyticsworld.com/patimes/pinpointing-the-persuadables-convincing-the-right-voters-to-support-barack-obama/ , accessed 1 March 2013 (available with free subscription).
Radcliffe, N.J. (2007a) Using control groups to target on predicted lift. DMA Analytic Annual Journal (Spring): 14–21.
Radcliffe, N.J. (2007b) Generating Incremental Sales: Maximizing the Incremental Impact of Cross-Selling, Up-Selling and Deep-Selling Through Uplift Modelling. Stochastic Solutions Limited.
Radcliffe, N.J. and Surry, P.D. (1999) Differential response analysis: Modeling true response by isolating the effect of a single action. Proceedings of Credit Scoring and Credit Control VI, Credit Research Centre, University of Edinburgh Management School.
Radcliffe, N.J. and Surry, P.D. (2011) Real-world uplift modelling with significance-based uplift trees. Portrait Technical Report TR-2011-1 and Stochastic Solutions White Paper 2011. http://stochasticsolutions.com/pdf/sig-based-up-trees.pdf , accessed 31 December 2011.
Rexer, K. (2012) 5th Annual Data Mining Survey – 2011 Survey Summary Report. Rexer Analytics.
Rexer, K. (2013) 6th Annual Data Mining Survey – 2012 Survey Summary Report. Rexer Analytics.
Samuelson, D.A. (2013) Analytics: Key to Obama’s victory. OR/MS Today February: 20–24.
Scherer, M. (2012) How Obama’s data crunchers helped him win. CNN News. http://www.cnn.com/2012/11/07/tech/web/obama-campaign-tech-team/index.html?hpt=hp_bn5 , accessed 7 November 2012.
Siegel, E. (2011) Upilft Modeling: Predictive Analytics Can’t Optimize Marketing Decisions Without It. Prediction Impact white paper sponsored by Pitney Bowes Business Insight.
Siegel, E. (2013a) The real story behind Obama’s election victory. The Fiscal Times 01/21/2013. http://www.thefiscaltimes.com/Articles/2013/01/21/The-Real-Story-Behind-Obamas-Election-Victory.aspx#page1 , accessed 31 January 2013.
Siegel, E. (2013b) Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, New Jersey: Wiley.
Siroker, D. and Koomen, P. (2013) A/B Testing: The Most Powerful Way to Turn Clicks Into Customers, Hoboken, NJ: Wiley.
Storey, A. and Cohen, M. (2002) Exploiting response models: Optimizing cross-sell and up-sell opportunities in banking. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 325–331, ACM, New York, NY.
Taha, H.A. (2010) Operations Research. 9th edn. Prentice-Hall.
Williams, H.P. (2003) Model Building in Mathematical Programming. 4th edn. Wiley.
Yong, F.H. (2015) Quantitative methods for stratified medicine. PhD dissertation, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University.
Zenios, S.A. (2007) Practical Financial Optimization: Decision Making for Financial Engineers, Malden, MA: Blackwell Publishing.