Selectively Acquiring Customer Information: A New Data Acquisition Problem and an Active Learning-Based Solution
Published Online:1 May 2006https://doi.org/10.1287/mnsc.1050.0488
References
- The usefulness of optimum experimental designs. J. Roy. Statist. Soc. (1996) 58:59–76Google Scholar
- One hundred years of the design of experiments on and off the pages of Biometrika. Biometrika (2001) 88:53–97Crossref, Google Scholar
- Optimum Experimental Designs (1992) (Oxford Science Publications, Oxford, UK) Google Scholar
- UCI repository of machine learning databases. (1998) (Department of Computer Science, University of California, Irvine, CA) . http://www.ics.uci.edu/∼mlearn/MLRepository.htmlGoogle Scholar
- Bayesian Inference in Statistical Analysis (1992) (Addison-Wesley, Reading, MA) Crossref, Google Scholar
- Statistics for Experimenters (1978) (John Wiley & Sons, New York) Google Scholar
- Optimal Bayesian design applied to logistic regression experiments. J. Statist. Planning Inference (1989) 21:191–208Crossref, Google Scholar
- Bayesian experimental design: A review. Statist. Sci. (1995) 10:273–304Crossref, Google Scholar
- Survey Sampling: Theory and Methods (1992) (Marcel Dekker Inc., Florence, KY.) Google Scholar
- Sampling Techniques (1977) 3rd ed.(John Wiley & Sons, New York) Google Scholar
- Neural network exploration using optimal experiment design. J. Econometrics (1996) 37:87–114Google Scholar
- Active learning with statistical models. J. Artificial Intelligence Res. (1996) 4:129–143Crossref, Google Scholar
- Quasi-Experimentation: Design and Analysis Issues for Field Settings (1979) (Rand McNally College Publishing Company, Chicago, IL) Google Scholar
- On the equivalence of constrained and compound optimal designs. J. Amer. Statist. Assoc. (1994) 89:426–434Crossref, Google Scholar
- Ensemble methods in machine learning. Multiple Classifier Systems (2000) 18:1–15Crossref, Google Scholar
- Expert system design: Minimizing information acquisition costs. Decision Support Systems (1993) 9:161–181Crossref, Google Scholar
- An empirical investigation of credit card default: Ponzi schemes and other behaviors. (2004) . Working paper, Department of Economics, Ohio State University, Columbus, OHGoogle Scholar
- Committee-based sample selection for probabilistic classifiers. J. Artificial Intelligence Res. (1999) 11:335–360Crossref, Google Scholar
- A sequentially constructed design for estimating nonlinear parameter functions. Biometrika (1980) 67:381–388Crossref, Google Scholar
- Selective sampling using query by committee algorithm. Mach. Learning (1997) 28:133–168Crossref, Google Scholar
- Econometric Analysis (2000) (Prentice Hall, Upper Saddle River, NJ) Google Scholar
- Learning cost-sensitive active classifiers. Artificial Intelligence (2002) 139:137–174Crossref, Google Scholar
- Active learning in neural networks. (1999) . Working paper, University of Bielefeld, Bielefeld, Germany, http://citeseer.nj.nec.com/404108.htmlGoogle Scholar
- On sequential designs in non-linear problems. Biometrika (1998) 85:496–503Crossref, Google Scholar
- The value of private sector credit information sharing: The U.S. case. J. Banking Finance (2003) 27:449–469Crossref, Google Scholar
- Optimum experimental design. J. Roy. Statist. Soc., Ser. B (1959) 21:272–304Google Scholar
- Efficient experimental design with marketing research applications. J. Marketing Res. (1994) 31:545–557Crossref, Google Scholar
- Information Theory and Statistics (1959) (Wiley, New York) Google Scholar
- Bayesian Statistics—A Review (1972) (SIAM, Philadelphia, PA) Crossref, Google Scholar
- Statistical Analysis with Missing Data (1987) (John Wiley & Sons, New York) Google Scholar
- Information-based objective functions for active data selection. Neural Comput. (1992) 4:590–604Crossref, Google Scholar
- . Pentagon plans a computer system that would peek at personal data of Americans. New York Times (2002) November 9Google Scholar
- Active feature-value acquisition for classification induction. Proc. ICDM-2004, Brighton, UK (2004) (IEEE Computer Society)483–486Google Scholar
- Inductive expert system design: Maximizing system value. Inform. Systems Res. (1993) 4:111–131Link, Google Scholar
- Redesigning case retrieval to reduce information acquisition costs. Inform. Systems Res. (1997) 8:51–69Link, Google Scholar
- A model of decision-making with sequential information-acquisition (Part 1). Decision Support Systems (1986) 2:285–307Crossref, Google Scholar
- A model of decision-making with sequential information-acquisition (Part 2). Decision Support Systems (1987) 3:47–73Crossref, Google Scholar
- Information acquisition policies for resources allocation among multiple agents. Inform. Systems Res. (1997) 8:151–181Link, Google Scholar
- An empirical analysis of the value of complete information for eCRM models. MIS Quart. (2005) . ForthcomingGoogle Scholar
- Modeling browsing behavior at multiple websites. Marketing Sci. (2004) 23:280–303Link, Google Scholar
- ACORA: Distribution-based aggregation for relational learning from identified attributes. (2004) . Working paper CeDER-04-04, Stern School of Business, New York University, New YorkGoogle Scholar
- On the use of generalized linear models following a sequential design. Statist. Probab. Lett. (2002) 56:155–161Crossref, Google Scholar
- On finite population sampling theory under certain linear regression models. Biometrika (1970) 57:377–387Crossref, Google Scholar
- Likelihood functions in finite population sampling theory. Biometrika (1976) 63:605–614Crossref, Google Scholar
- Multiple Imputation for Nonresponse in Surveys (1987) (J. Wiley & Sons, New York) Crossref, Google Scholar
- Missing data, data imputation, bootstrap. J. Amer. Statist. Assoc. (1994) 89:426–434Crossref, Google Scholar
- Active learning for class probability estimation and ranking. Proc. Internat. Joint Conf. Artificial Intelligence (IJCAI 01), Seattle, WA (2001) (American Association for Artificial Intelligence)911–920Google Scholar
- Multiple imputation for multivariate missing-data problems: A data analyst’s perspective. Multivariate Behavioral Res. (1998) 33:545–571Crossref, Google Scholar
- Practical PAC learning. Proc. 14th Internat. Conf. Artificial Intelligence (IJCAI 95), Montreal, Canada (1995) (American Association for Artificial Intelligence)1–7Google Scholar
- , Gregg L. W. Problem solving and rule induction: A unified view. Knowledge and Cognition (1974) (Erlbaum, Potomac, MD) . Chapter 5Google Scholar
- Elements of Survey Sampling (1996) (Kluwer Academic Publishers, Amsterdam, Netherlands) Crossref, Google Scholar
- Populations and selection: Limitations of statistics. J. Roy. Statist. Soc. (Ser. A) (1993) 156:144–166Crossref, Google Scholar
- Biometrika centenary: Sample surveys. Biometrika (2001) 88:167–194Crossref, Google Scholar
- A controlled donor imputation system for a one-number census. J. Roy. Statist. Soc. (Ser. A) (2002) 165:495–522Crossref, Google Scholar
- Multiple Criteria Optimization: Theory, Computation and Application (1986) (Wiley, New York) Google Scholar
- Active learning for structure in Bayesian networks. Proc. Internat. Joint Conf. Artificial Intelligence 2001, Seattle, WA (2001) (American Association for Artificial Intelligence)647–653Google Scholar
- A theory of the learnable. Comm. ACM (1984) 27:1134–1142Crossref, Google Scholar
- Asymptotic inference from sequential design in a non-linear situation. Biometrika (1985) 72:552–558Crossref, Google Scholar
- On active learning for data acquisition. Proc. IEEE Internat. Conf. Data Mining 2002 (2002) 562–569Crossref, Google Scholar
- Constructing ensembles from data envelopment analysis. INFORMS J. Comput. (2005) . ForthcomingGoogle Scholar

