Market Share Constraints and the Loss Function in Choice-Based Conjoint Analysis

References

• Aitchison J., Silvey S. D. Maximum-likelihood estimation of parameters subject to restraints. Ann. Math. Statist. (1958) 29(3):813–828Crossref, Google Scholar
• Allenby G. M., Arora N., Ginter J. L. Incorporating prior knowledge into the analysis of conjoint studies. J. Marketing Res. (1995) 32(May):152–162Crossref, Google Scholar
• Allenby G., Fennell G., Huber J., Eagle T., Gilbride T., Horsky D., Kim J., Lenk P., Johnson R., Olfek E., Orme B., Walker J. Adjusting choice models to better predict market behavior. Marketing Lett. (2005) 16(3/4):197–208Crossref, Google Scholar
• Anderson J. A., Blair V. Penalized maximum likelihood estimation in logistic regression and discrimination. Biometrika (1982) 69(1):123–136Crossref, Google Scholar
• Berger J. O.Statistical Decision Theory and Bayesian Analysis (1985) 2nd ed.(Springer-Verlag, New York) Crossref, Google Scholar
• Bernardo J. M., Smith A. F. M.Bayesian Theory (1994) (John Wiley & Sons, New York) Crossref, Google Scholar
• Boatwright P., McCulloch R., Rossi P. Account-level modeling for trade promotion: An application of a constrained parameter hierarchical model. J. Amer. Statist. Assoc. (1999) 94(448):1063–1073Crossref, Google Scholar
• Casella G., Berger R. L.Statistical Inference (1990) (Duxbury Press, Belmont, CA) Google Scholar
• Cattin P., Wittink D. R. Commercial use of conjoint analysis: A survey. J. Marketing Res. (1982) 46(Summer):44–53Crossref, Google Scholar
• Chib S., Greenberg E. Understanding the metropolis-hastings algorithm. Amer. Statist. (1995) 49(November):327–335Google Scholar
• Craven P., Wahba G. Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik (1979) 31:377–403Crossref, Google Scholar
• DeGroot M.Optimal Statistical Decision (1970) (McGraw-Hill, New York) Google Scholar
• Ding M., Grewal R., Liechty J. Incentive-aligned conjoint analysis. J. Marketing Res. (2005) 42(February):67–82Crossref, Google Scholar
• Dupacova J., Wets R. Asymptotic-behavior of statistical estimators and of optimal solutions of stochastic optimization problems. Ann. Statist. (1988) 16(4):1517–1549Crossref, Google Scholar
• Evgeniou T., Pontil M., Poggio T. Regularization networks and support vector machines. Adv. Comput. Math. (2000) 13:1–50Crossref, Google Scholar
• Evgeniou T., Pontil M., Toubia O. A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Marketing Sci. (2007) 26(6):805–818Link, Google Scholar
• Geisser S. Predictive sample reuse method with applications. J. Amer. Statist. Assoc. (1975) 70(350):320–328Crossref, Google Scholar
• Ghosh M. Constrained Bayes estimation with applications. J. Amer. Statist. Assoc. (1992) 87(418):533–540Crossref, Google Scholar
• Golub G. H., Heath M., Wahba G. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics (1979) 21(2):215–223Crossref, Google Scholar
• Good I. J., Gaskins R. A. Nonparametric roughness penalties for probability densities. Biometrika (1971) 58(2):255–271Crossref, Google Scholar
• Good I. J., Gaskins R. A. Density estimation and bump-hunting by the penalized likelihood method exemplified by scattering and meteorite data. J. Amer. Statist. Assoc. (1980) 75(369):42–56Crossref, Google Scholar
• Kimeldorf G. S., Wahba G. A correspondence between Bayesian estimation on stochastic processes and smoothing by splines. Ann. Math. Statist. (1970) 41(2):495–501Crossref, Google Scholar
• Lenk P. The logistic normal distribution for Bayesian, nonparametric, predictive densities. J. Amer. Statist. Assoc. (1988) 83(402):509–516Crossref, Google Scholar
• Lenk P. Bayesian inference of semiparametric regression. J. Roy. Statist. Soc. Ser. B (1999) 61:863–879Crossref, Google Scholar
• Lenk P. Bayesian semiparametric density estimation and model verification using a logistic-gaussian process. J. Comput. Graphical Statist. (2003) 12(3):548–565Crossref, Google Scholar
• Lenk P., Wayne D., Paul G., Martin Y. Hierarchical Bayes conjoint analysis: Recovery of partworth heterogeneity from reduced experimental designs. Marketing Sci. (1996) 15(2):173–191Link, Google Scholar
• Liew C. K. Inequality constrained least-squares estimation. J. Amer. Statist. Assoc. (1976) 71(355):746–751Crossref, Google Scholar
• Louis T. A. Estimating a population of parameter values using Bayes and empirical Bayes methods. J. Amer. Statist. Assoc. (1984) 79(386):393–398Crossref, Google Scholar
• Louviere J. J. What if consumer experiments impact variances as well as means? Response variability as a behavioral phenomenon. J. Consumer Res. (2001) 28(3):506–511Crossref, Google Scholar
• McCulloch R., Rossi P. E. An exact likelihood analysis of the multinomial probit model. J. Econometrics (1994) 64:207–240Crossref, Google Scholar
• Morikawa T., Ben-Akiva M., McFadden D. Discrete choice models incorporating revealed preferences and psychometric data. Econom. Models Marketing (2002) 16:29–55Google Scholar
• Orme B., Johnson R. External effect adjustments in conjoint analysis. (2006) . Working paper, Sawtooth Software, Sequim, WA. http://www.sawtoothsoftware.com/download/techpop/externaleffects.pdfGoogle Scholar
• Savage L. J.The Foundations of Statistics (1954) (John Wiley & Sons, New York) Google Scholar
• Shen W., Louis T. A. Triple goal estimates in two-stage hierarchical models. J. Roy. Statist. Soc. Ser. B (1998) 60(2):455–471Crossref, Google Scholar
• Smith A. F. M., Gelfand A. E. Bayesian statistics without tears: A sampling-resampling perspective. Amer. Statist. (1992) 46(May):84–88Google Scholar
• Stone M. Cross-validatory choice and assessment of statistical prediction. J. Roy. Statist. Soc. Ser. B (1974) 36(2):111–147Google Scholar
• Wahba G. Bayesian “confidence intervals” for the cross-validated smoothing spline. J. Roy. Statist. Soc. Ser. B (1983) 45(1):133–150Google Scholar
• Wang J. D. Asymptotics of least-squares estimators for constrained nonlinear regression. Ann. Statist. (1996) 24(3):1316–1326Crossref, Google Scholar