Help
Cart
Skip main navigation

Available Issues

April 10, 2013 - May 11, 2026

Available Issues

April 10, 2013 - May 11, 2026

Explaining Preference Heterogeneity with Mixed Membership Modeling

Published Online:3 Mar 2020https://doi.org/10.1287/mksc.2019.1185

References

  • Airoldi EM, Blei DM, Erosheva EA, Fienberg SE (2014) Handbook of Mixed Membership Models and Their Applications, 1st ed. (Chapman & Hall/CRC, Boca Raton, FL).Google Scholar
  • Allenby GM, Ginter JL (1995) Using extremes to design products and segment markets. J. Marketing Res. 32(4):392–403.Crossref, Google Scholar
  • Allenby GM, Rossi PE (1998) Marketing models of consumer heterogeneity. J. Econometrics 89(1–2):57–78.Crossref, Google Scholar
  • Allenby GM, Arora N, Ginter JL (1998) On the heterogeneity of demand. J. Marketing Res. 35(3):384–389.Crossref, Google Scholar
  • Ansari A, Li Y, Zhang JZ (2018) Probabilistic topic model for hybrid recommender systems: A stochastic variational Bayesian approach. Marketing Sci. 37(6):987–1008.Link, Google Scholar
  • Antoniak CE (1974) Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Ann. Statist. 2(6):1152–1174.Crossref, Google Scholar
  • Archak N, Ghose A, Ipeirotis PG (2011) Deriving the pricing power of product features by mining consumer reviews. Management Sci. 57(8):1485–1509.Link, Google Scholar
  • Blei DM, McAuliffe JD (2007) Supervised topic models. Platt JC, Koller D, Singer Y, Roweis ST, eds. Proc. 20th Internat. Conf. Neural Inform. Processing Systems (Curran Associates, Red Hook, NY), 121–128.Google Scholar
  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J. Machine Learn. Res. 3:993–1022.Google Scholar
  • Büschken J, Allenby GH (2016) Sentence-based text analysis for customer reviews. Marketing Sci. 35(6):953–975.Link, Google Scholar
  • Chandukala SR, Edwards YD, Allenby GM (2011) Identifying unmet demand. Marketing Sci. 30(1):61–73.Link, Google Scholar
  • Clive J, Woodbury MA, Siegler IC (1983) Fuzzy and crisp set-theoretic-based classification of health and disease. J. Medical Systems 7(4):317–332.Crossref, Google Scholar
  • Eddelbuettel D, Francois R, Allaire JJ, Ushey K, Kou Q, Russell N, Bates D, Chambers J (2018) Rcpp: Seamless R and C++ integration. Accessed August 28, 2019, https://CRAN.R-project.org/package=Rcpp.Google Scholar
  • Erosheva EA, Fienberg SE, Joutard C (2007) Describing disability through individual-level mixture models for multivariate binary data. Ann. Appl. Stat. 1(2):346–384.Crossref, Google Scholar
  • Eugster MJA, Leisch F, Seth S (2014) archetypes: Archetypal analysis. Accessed August 28, 2019, https://CRAN.R-project.org/package=archetypes.Google Scholar
  • Ferguson TS (1973) A Bayesian analysis of some nonparametric problems. Ann. Statist. 1(2):209–230.Crossref, Google Scholar
  • Frühwirth-Schnatter S (2006) Finite Mixture and Markov Switching Models (Springer-Verlag, New York).Google Scholar
  • Galyardt A (2014) Interpreting mixed membership. Handbook of Mixed Membership Models and Their Applications (Chapman & Hall/CRC, Boca Raton, FL), 39–65.Google Scholar
  • George EI, McCulloch RE (1993) Variable selection via Gibbs sampling. J. Amer. Statist. Assoc. 88(423):881–889.Crossref, Google Scholar
  • Green PJ (1995) Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82(4):711–732.Crossref, Google Scholar
  • Green PJ, Łatuszyński K, Pereyra M, Robert CP (2015) Bayesian computation: A summary of the current state, and samples backward and forwards. Statist. Comput. 25(4):835–862.Crossref, Google Scholar
  • Gross JH, Manrique-Vallier D (2014) A mixed-membership approach to the assessment of political ideology from survey responses. Handbook of Mixed Membership Models and Their Applications (Chapman & Hall/CRC, Boca Raton, FL), 119–139.Google Scholar
  • Horsky D, Misra S, Nelson P (2006) Observed and unobserved preference heterogeneity in brand-choice models. Marketing Sci. 25(4):322–335.Link, Google Scholar
  • Jacobs BJD, Donkers B, Fok D (2016) Model-based purchase predictions for large assortments. Marketing Sci. 35(3):389–404.Google Scholar
  • Joutard C, Airoldi EM, Fienberg SE, Love TM (2007) Discovery of latent patterns with hierarchical Bayesian mixed-membership models and the issue of model choice. Masseglia F, Teisseire M, eds. Data Mining Patterns: New Methods and Applications (IGI Global, Hershey, PA), 240–275.Google Scholar
  • Kamakura WA, Russell GJ (1989) A probabilistic choice model for market segmentation and elasticity structure. J. Marketing Res. 26(4):379–390.Crossref, Google Scholar
  • Lee SY (2007) Structural Equation Modeling: A Bayesian Approach (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
  • Lee TY, Bradlow ET (2011) Automated marketing research using online customer reviews. J. Marketing Res. 48(5):881–894.Crossref, Google Scholar
  • Lenk PJ, DeSarbo WS, Green PE, Young MR (1996) Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. 15(2):173–191.Link, Google Scholar
  • Manton KG, Woodbury MA, Tolley HD (1994) Statistical Application Using Fuzzy Sets (Wiley, New York).Google Scholar
  • Marini MM, Li X, Fan PL (1996) Characterizing latent structure: Factor analytic and grade of membership models. Sociol. Methodology 26:133–164.Crossref, Google Scholar
  • Netzer O, Feldman R, Goldenberg J, Fresko M (2012) Mine your own business: Market-structure surveillance through text mining. Marketing Sci. 31(3):521–543.Link, Google Scholar
  • Newton MA, Raftery AE (1994) Approximate Bayesian inference with the weighted likelihood bootstrap. J. Royal Statist. Soc. B 56(1):3–48.Google Scholar
  • Rossi PE (2018) bayesm: Bayesian inference for marketing/micro-econometrics. Accessed August 28, 2019, https://CRAN.R-project.org/package=bayesm.Google Scholar
  • Rossi PE, Allenby GM (2003) Bayesian statistics and marketing. Marketing Sci. 22(3):304–328.Link, Google Scholar
  • Rossi PE, Allenby GM, McCulloch RE (2005) Bayesian Statistics and Marketing (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Rossi PE, Gilula Z, Allenby GM (2001) Overcoming scale usage heterogeneity: A Bayesian hierarchical approach. J. Amer. Statist. Assoc. 96(453):20–31.Crossref, Google Scholar
  • Rossi PE, McCulloch RE, Allenby GM (1996) The value of purchase history data in target marketing. Marketing Sci. 15(4):321–340.Link, Google Scholar
  • Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J. Royal Statist. Soc. B 64(4):583–639.Crossref, Google Scholar
  • Stewart DW (1981) The application and misapplication of factor analysis in marketing research. J. Marketing Res. 18(1):51–62.Crossref, Google Scholar
  • Tirunillai S, Tellis GJ (2014) Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. J. Marketing Res. 51(4):463–479.Crossref, Google Scholar
  • Toubia O, Netzer O (2017) Idea generation, creativity, and prototypicality. Marketing Sci. 36(1):1–20.Link, Google Scholar
  • Vehtari A, Gabry J, Yao Y, Gelman A (2018) loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models. Accessed August 28, 2019, https://CRAN.R-project.org/package=loo.Google Scholar
  • Watanabe S (2010) Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J. Machine Learn. Res. 11:3571–3594.Google Scholar
  • Woodbury MA, Clive J, Garson A Jr (1978) Mathematical typology: A grade of membership technique for obtaining disease definition. Comput. Biomedical Res. 11(3):277–298.Crossref, Google Scholar
Previous
Back to Top
Next
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.
Agree