No Customer Left Behind: A Distribution-Free Bayesian Approach to Accounting for Missing Xs in Marketing Models

References

• Albert J. H., Chib S. Bayesian analysis of binary and polychotomous response data. J. Amer. Statist. Assoc. (1993) 88(422):669–679Crossref, Google Scholar
• Allenby G. M., Lenk P. J. Modeling household purchase behavior with logistic normal regression. J. Amer. Statist. Assoc. (1994) 89(428):1218–1231Crossref, Google Scholar
• Allenby G. M., Rossi P. E. Marketing models of consumer heterogeneity. J. Econometrics (1999) 89(1–2):57–78Crossref, Google Scholar
• Blattberg R. C., Kim B.-D., Neslin S. A.Database Marketing: Analyzing and Managing Customers (2008) (Springer, New York) Crossref, Google Scholar
• Bradlow E. T., Zaslavsky A. M. A hierarchical latent variable model for ordinal data from a customer satisfaction survey with “no answer” responses. J. Amer. Statist. Assoc. (1999) 94(445):43–52Google Scholar
• Bradlow E. T., Hu Y., Ho T.-H. A learning-based model for imputing missing levels in partial conjoint profiles. J. Marketing Res. (2004) 41(4):369–381Crossref, Google Scholar
• Chen H. Y. Nonparametric and semiparametric models for missing covariates in parametric regression. J. Amer. Statist. Assoc. (2004) 99(468):1176–1189Crossref, Google Scholar
• Chiang J. Competing coupon promotions and category sales. Marketing Sci. (1995) 14(1):105–122Link, Google Scholar
• Chintagunta P. K., Jain D. C., Vilcassim N. J. Investigating heterogeneity in brand preferences in logit models for panel data. J. Marketing Res. (1991) 28(4):417–428Crossref, Google Scholar
• Daniels M. J., Hogan J. W.Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (2008) (Chapman & Hall, London) Crossref, Google Scholar
• Duane S., Kennedy A. D., Pendleton B. J., Roweth D. Hybrid Monte Carlo. Phys. Lett. B (1987) 195(2):216–222Crossref, Google Scholar
• Erdem T., Keane M. P., Sun B. Missing price and coupon availability data in scanner panels: Correcting for the self-selection bias in choice model parameters. J. Econometrics (1999) 89(1–2):177–196Crossref, Google Scholar
• Feit E. M., Beltramo M. A., Feinberg F. M. Reality check: Combining choice experiments with market data to estimate the importance of product attributes. Management Sci. (2010) 56(5):785–800Link, Google Scholar
• Geweke J., Bernardo J. M., Berger J. O., Dawid A. P., Smith A. F. M. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. Bayesian Statistics 4: Proceedings of the Fourth Valencia International Meeting (1992) (Clarendon Press, Oxford, UK) 169–194Google Scholar
• Gilula Z., McCulloch R. E., Rossi P. E. A direct approach to data fusion. J. Marketing Res. (2006) 43(1):73–83Crossref, Google Scholar
• Gönül F., Srinivasan K. Modeling multiple sources of heterogeneity in multinomial logit models: Methodological and managerial issues. Marketing Sci. (1993) 12(3):213–229Link, Google Scholar
• Guadagni P. M., Little J. D. C. A logit model of brand choice calibrated on scanner data. Marketing Sci. (1983) 2(3):203–238Link, Google Scholar
• Gupta S. Stochastic models of interpurchase time with time-dependent covariates. J. Marketing Res. (1991) 28(1):1–15Crossref, Google Scholar
• Ibrahim J. G., Chen M.-H., Lipsitz S. R., Herring A. H. Missing-data methods for generalized linear models: A comparative review. J. Amer. Statist. Assoc. (2005) 100(469):332–346Crossref, Google Scholar
• Kamakura W. A., Russell G. J. A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. (1989) 26(4):379–390Crossref, Google Scholar
• Kamakura W. A., Wedel M. Statistical data-fusion for cross-tabulation. J. Marketing Res. (1997) 34(4):485–498Crossref, Google Scholar
• Kamakura W. A., Wedel M. Factor analysis and missing data. J. Marketing Res. (2000) 37(4):490–498Crossref, Google Scholar
• Kang J. D. Y., Schafer J. L. Demystifying double robustness: A comparison of alternative strategies for estimating population mean from incomplete data (with discussion). Statist. Sci. (2007) 22(4):523–539Crossref, Google Scholar
• Khan R., Lewis M., Singh V. Dynamic customer management and the value of one-to-one marketing. Marketing Sci. (2009) 28(6):1063–1079Link, Google Scholar
• Little R. J. A. Regression with missing X's: A review. J. Amer. Statist. Assoc. (1992) 87(420):1127–1137Google Scholar
• Little R. J. A., Rubin D. B.Statistical Analysis with Missing Data (2002) (John Wiley & Sons, New York) Crossref, Google Scholar
• Meier P., Karrison T., Chappell R., Xie H. The price of Kaplan–Meier. J. Amer. Statist. Assoc. (2004) 99(467):890–896Crossref, Google Scholar
• Musalem A., Bradlow E. T., Raju J. S. Who's got the coupon? Estimating consumer preferences and coupon usage from aggregate information. J. Marketing Res. (2008) 45(6):715–730Crossref, Google Scholar
• Qian Y. Do national patent laws stimulate domestic innovation in a global patenting environment? A cross-country analysis of pharmaceutical patent protection, 1978–2002. Rev. Econom. Statist. (2007) 89(3):436–453Crossref, Google Scholar
• Qian Y., Xie H. Measuring the impact of nonignorability in panel data with non-monotone nonresponse. J. Appl. Econometrics (2010) . ePub ahead of print February 1, http://onlinelibrary.wiley.com/doi/10.1002/jae.1157/abstractGoogle Scholar
• Raftery A. E., Newton M. A., Satagopan J. M., Krivitsky P. N. Estimating the integrated likelihood via posterior simulation using the harmonic mean identity. Bayesian Statist. (2007) 8:1–45Google Scholar
• Rossi P. E., Allenby G. M. A Bayesian approach to estimating household parameters. J. Marketing Res. (1993) 30(2):171–182Crossref, Google Scholar
• Rossi P. E., Allenby G. M., McCulloch R.Bayesian Statistics and Marketing (2005) (John Wiley & Sons, London) Crossref, Google Scholar
• Rubin D. B. Inference and missing data (with discussion). Biometrika (1976) 63(3):581–592Crossref, Google Scholar
• Schafer J. L.Analysis of Incomplete Multivariate Data (1997) (Chapman & Hall, London) Crossref, Google Scholar
• Schafer J. L., Graham J. W. Missing data: Our view of the state of the art. Psych. Methods (2002) 7(2):147–177Crossref, Google Scholar
• Seetharaman P. B., Chintagunta P. K. The proportional hazards model for purchase timing: A comparison of alternative specifications. J. Bus. Econom. Statist. (2003) 21(3):368–382Crossref, Google Scholar
• Singh V. P., Hansen K. T., Blattberg R. C. Market entry and consumer behavior: An investigation of a Wal-Mart supercenter. Marketing Sci. (2006) 25(5):457–476Link, Google Scholar
• Tierney L. Markov chains for exploring posterior distributions. Ann. Statist. (1994) 22(4):1701–1728Crossref, Google Scholar
• Tsiatis A. A.Semiparametric Theory and Missing Data (2006) (Springer, New York) Google Scholar
• Wedel M., Kamakura W. A., Desarbo W. S., Hofstede F. T. Implications for asymmetry, nonproportionality, and heterogeneity in brand switching from piece-wise exponential mixture hazard models. J. Marketing Res. (1995) 32(4):457–462Crossref, Google Scholar
• Yang S., Zhao Y., Dhar R. Modeling the underreporting bias in panel survey data. Marketing Sci. (2010) 29(3):525–539Link, Google Scholar
• Ying Y., Feinberg F., Wedel M. Leveraging missing ratings to improve online recommendation systems. J. Marketing Res. (2006) 43(3):355–365Crossref, Google Scholar