Bayesian Statistics and Marketing
Published Online:1 Aug 2003https://doi.org/10.1287/mksc.22.3.304.17739
References
- . Similarities in choice behavior across product categories. Marketing Sci. (1998) 17:91–106Link, Google Scholar
- Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. (1995) 32:152–162Crossref, Google Scholar
- On the heterogeneity of demand. J. Marketing Res. (1998) 35:384–389Crossref, Google Scholar
- Using extremes to design products and segment markets. J. Marketing Res. (1995) 32:392–403Crossref, Google Scholar
- Modeling household purchase behavior with logistic normal regression. J. Amer. Statist. Assoc. (1994) 89:1218–1231Crossref, Google Scholar
- Reassessing brand loyalty, price sensitivity, and merchandising effects on consumer brand choice. J. Bus. Econom. Statist. (1995) 13:281–289Crossref, Google Scholar
- . A dynamic model of purchase timing with application to direct marketing. J. Amer. Statist. Assoc. (1999) 94:365–374Crossref, Google Scholar
- Marketing models of consumer heterogeneity. J. Econometrics (1999) 89:57–78Crossref, Google Scholar
- . Hierarchical Bayes versus finite mixture conjoint analysis models: A comparison of fit, prediction, and partworth recovery. J. Marketing Res. (2002) 87–98Crossref, Google Scholar
- . Internet recommendation systems. J. Marketing Res. (2000a) 37:363–375Crossref, Google Scholar
- . A hierarchical Bayesian methodology for treating heterogeneity in structural equation models. Marketing Sci. (2000b) 19:328–347Link, Google Scholar
- Measuring the influence of individual preference structures in group decision making. J. Marketing Res. (1999) 36:476–487Crossref, Google Scholar
- A hierarchical Bayes model of primary and secondary demand. Marketing Sci. (1998) 17:29–44Link, Google Scholar
- . Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Statistica Sinica (2000) 10:4–24Google Scholar
- Bayesian Theory (1994) (John Wiley, New York) Crossref, Google Scholar
- Shrinkage estimation of price and promotional elasticities: Seemingly unrelated equations. J. Amer. Statist. Assoc. (1991) 86:304–315Crossref, Google Scholar
- Account-level modeling for trade promotion: An application of a constrained parameter hierarchical model. J. Amer. Statist. Assoc. (1999) 94:1063–1073Crossref, Google Scholar
- A Bayesian lifetime model for the "Hot 100". Billboard songs J. Amer. Statist. Assoc. (2001) 96:368–381Crossref, Google Scholar
- A hierarchical Bayes model for assortment choice. J. Marketing Res. (2000) 37:259–268Crossref, Google Scholar
- The impact of heterogeneity in purchase timing and price responsiveness on estimates of sticker shock effects. Marketing Sci. (1999) 18:178–192Link, Google Scholar
- . Markov chain Monte Carlo and models of consideration set and parameter heterogeneity. J. Econometrics (1999) 89:223–248Crossref, Google Scholar
- , Fienberg S. E., Kadane J. B. Monte Carlo methods and Bayesian computation: Overview. International Encyclopedia of the Social and Behavioral Sciences: Statistics (2003) . Elsevier Science, Amsterdam, The Netherlands. In pressGoogle Scholar
- . A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data. J. Econometrics (1999) 89:79–108Crossref, Google Scholar
- . Multiple discreteness and product differentiation: Demand for carbonated soft drinks. Marketing Sci. (2003) . ForthcomingGoogle Scholar
- Multivariate analysis of multiple response data. J. Marketing Res. (2002) . ForthcomingGoogle Scholar
- . Data analysis using Stein's estimator and its generalizations. J. Amer. Statist. Assoc. (1975) 70:311–319Crossref, Google Scholar
- . Brand and quantity choice dynamics under price uncertainty. Quantitative Marketing Econom. (2003) 1:5–64Crossref, Google Scholar
- . Bayesian analysis of the heterogeneity model. J. Bus. Econom. Statist. (2003) . ForthcomingGoogle Scholar
- Sampling-based approaches to calculating marginal densities. J. Amer. Statist. Assoc. (1990) 87(June):523–532Google Scholar
- Bayesian Data Analysis (1995) (Chapman Hall, London) Crossref, Google Scholar
- . Attribute-based consideration sets. (2003) . Working paper, Ohio State UniversityGoogle Scholar
- . A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica (1982) 52:271–320Crossref, Google Scholar
- . On the similiarity of classical and Bayesian estimates of individual mean partworths. Marketing Lett. (2001) 12:259–269Crossref, Google Scholar
- A Bayesian approach to modeling purchase frequency. Marketing Lett. (2003) 14:5–20Crossref, Google Scholar
- . Pricing decision under demand uncertainty: A Bayesian mixture model approach. Marketing Sci. (1996) 15:207–221Link, Google Scholar
- Estimating irregular pricing effects: A stochastic spline regression approach. J. Marketing Res. (1998) 35:16–29Crossref, Google Scholar
- . Statistical data fusion for cross-tabulation. J. Marketing Res. (1997) 34:485–498Crossref, Google Scholar
- , Maddala G. S., Rao C. R., Vinod H. D. Simulation estimation methods for limited dependent variable models. Handbook of Statistics (1993) 11(North Holland, Amsterdam, The Netherlands)Google Scholar
- Modeling consumer demand for variety. Marketing Sci. (2002) 21:223–228Link, Google Scholar
- Choice menus for mass customization. J. Marketing Res. (2001) 38:183–196Crossref, Google Scholar
- Bayesian inference for finite mixtures of generalized linear models with random effects. Psychometrika (2000) 65:93–119Crossref, Google Scholar
- . New models from old: Forecasting product adoption by hierarchical Bayes procedures. Marketing Sci. (1990) 9:42–53Link, Google Scholar
- Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. (1996) 15:173–191Link, Google Scholar
- Monte Carlo Strategies in Scientific Computing (2001) (Springer-Verlag, New York) Google Scholar
- . The “shopping basket”: A model for multicategory purchase incidence decisions. Marketing Sci. (1999) 18:95–114Link, Google Scholar
- Response modeling with non-random marketing mix variables. (2003) . Working paper, Graduate School of Business, University of Chicago, Chicago, ILGoogle Scholar
- A unified approach to conjoint analysis models. J. Amer. Statist. Assoc. (2002) 97:674–682Crossref, Google Scholar
- . Bayesian analysis of the multinomial probit model with fully identified parameters. J. Econometrics (2000) 99:173–193Crossref, Google Scholar
- An exact likelihood analysis of the multinomial probit model. J. Econometrics (1994) 64:217–228Crossref, Google Scholar
- , Mariano Weeks, Schuermann. Bayesian analysis of multinomial probit model. Simulation-Based Inference in Econometrics (1999) (Cambridge University, Cambridge, U.K.) Google Scholar
- . Economic choices. Amer. Econom. Rev. (2001) 91:351–370Crossref, Google Scholar
- . Price uncertainty and consumer search: A structural model of consideration set formation. Marketing Sci. (2003) 22:58–84Link, Google Scholar
- Creating micro-marketing pricing strategies using supermarket scanner data. Marketing Sci. (1997) 16:315–337Link, Google Scholar
- Why analyst overconfidence about the functional form of demand models can lead to overpricing. Marketing Sci. (1999) 18:569–583Link, Google Scholar
- Estimating price elasticities with theory-based priors. J. Marketing Res. (1999) 36:413–423Crossref, Google Scholar
- . Parametric empirical bayes inference: Theory and applications. J. Amer. Statist. Assoc. (1983) 78:47–65Crossref, Google Scholar
- . A Bayesian model to forecast new product performance in domestic and international markets. Marketing Sci. (1999) 18:115–136Link, Google Scholar
- Approximate Bayesian inference by the weighted likelihood bootstrap (with discussion). J. Royal Statist. Soc. Series B (1994) 56:3–48Google Scholar
- . A hybrid Markov chain for the Bayesian analysis of the multinomial probit model. Statist. Comput. (1998) 8:229–242Crossref, Google Scholar
- . Unobserved preference changes in conjoint analysis. (2003) . Working paper, University of ViennaGoogle Scholar
- A Bayesian approach for estimating target market potential with limited geodemographic information. J. Marketing Res. (1996) 33:134–149Crossref, Google Scholar
- . Monte Carlo Statistical Methods (1999) (Springer, New York) Crossref, Google Scholar
- A Bayesian approach to estimating household parameters. J. Marketing Res. (1993) 30:171–182Crossref, Google Scholar
- Overcoming scale usage heterogeneity: A Bayesian hierarchical approach. J. Amer. Statist. Assoc. (2001) 96:20–31Crossref, Google Scholar
- The value of purchase history data in target marketing. Marketing Sci. (1996) 15:321–340Link, Google Scholar
- . Designing conjoint choice experiments using managers' prior beliefs. J. Marketing Res. (2001) 28:430–444Crossref, Google Scholar
- Sawtooth Software CBC hierarchical Bayes analysis technical paper. (2001) . Sawtooth Software Technical Paper Series, www.sawtoothsoftware.comGoogle Scholar
- . Estimating the dimension of a model. Ann. Statist. (1978) 6:461–464Crossref, Google Scholar
- . Investigating household state dependence effects across categories. J. Marketing Res. (1999) 36:488–500Crossref, Google Scholar
- . A non-parametric approach to identifying latent relationships in hierarchical models. Marketing Sci. (2000) 19:149–162Link, Google Scholar
- Massively categorical variables: Revealing the information in zip codes. Marketing Sci. (2002) 22:40–57Link, Google Scholar
- The calculation of posterior distributions by data augmentation. J. Amer. Statist. Assoc. (1987) 82:528–550Crossref, Google Scholar
- Identifying spatial segments in international markets. Marketing Sci. (2002) 21:160–177Link, Google Scholar
- . Bayesian prediction in hybrid conjoint analysis. J. Marketing Res. (2002) 34:253–261Crossref, Google Scholar
- . Markov chains for exploring posterior distributions. Ann. Statist. (1994) 23(4):1701–1728Crossref, Google Scholar
- . Eye fixations on advertisements and memory for brands: A model and findings. Marketing Sci. (2000) 19:297–312Link, Google Scholar
- A model for observation, structural, and household heterogeneity in panel data. Marketing Lett. (2000) 11:137–149Crossref, Google Scholar
- Modeling interdependent consumer preferences. J. Marketing Res. (2003) . ForthcomingCrossref, Google Scholar
- . Modeling variation in brand preference: The roles of objective environment and motivating conditions. Marketing Sci. (2002) 21:14–31Link, Google Scholar
- . Bayesian analysis of simultaneous demand and supply. (2003) . Working paper, Ohio State UniversityGoogle Scholar

