Ellipsoidal Methods for Adaptive Choice-Based Conjoint Analysis

References

• Abernethy J, Evgeniou T, Toubia O, Vert J (2008) Eliciting consumer preferences using robust adaptive choice questionnaires. IEEE Trans. Knowledge Data Engrg. 20(2):145–155.Crossref, Google Scholar
• Allenby GM, Brazell JD, Howell JR, Rossi PE (2014) Economic valuation of product features. Quant. Marketing Econom. 12(4):421–456.Crossref, Google Scholar
• Anderson TW (1984) An Introduction to Multivariate Statistical Analysis, 2nd ed. (John Wiley & Sons, New York).Google Scholar
• Arora N, Huber J (2001) Improving parameter estimates and model prediction by aggregate customization in choice experiments. J. Consumer Res. 28(2):273–283.Crossref, Google Scholar
• Bellman R (1957) Dynamic Programming, 1st ed. (Princeton University Press, Princeton, NJ).Google Scholar
• Bertsimas D, O’Hair A (2013) Learning preferences under noise and loss aversion: An optimization approach. Oper. Res. 61(5):1190–1199.Link, Google Scholar
• Bertsimas D, Tsitsiklis J (1997) Introduction to Linear Optimization, 1st ed. (Athena Scientific, Nashua, NH).Google Scholar
• Bezanson J, Edelman A, Karpinski S, Shah VB (2017) Julia: A fresh approach to numerical computing. SIAM Rev. 59(1):65–98.Crossref, Google Scholar
• Bliemer MC, Rose JM (2010) Construction of experimental designs for mixed logit models allowing for correlation across choice observations. Transportation Res. Part B: Methodological 44(6):720–734.Crossref, Google Scholar
• Boyd S, Vandenberghe L (2004) Convex Optimization (Cambridge University Press, New York).Crossref, Google Scholar
• Brazell JD, Diener CG, Karniouchina E, Moore WL, Séverin V, Uldry PF (2006) The no-choice option and dual response choice designs. Marketing Lett. 17(4):255–268.Crossref, Google Scholar
• Carpenter B, Gelman A, Hoffman M, Lee D, Goodrich B, Betancourt M, Brubaker MA, Guo J, Li P, Riddell A (2016) Stan: A probabilistic programming language. J. Statist. Software 20(1):1–37.Google Scholar
• Cohen M, Lobel I, Paes Leme R (2016) Feature-based dynamic pricing. Working paper, New York University, New York.Crossref, Google Scholar
• Crabbe M, Akinc D, Vandebroek M (2014) Fast algorithms to generate individualized designs for the mixed logit choice model. Transportation Res. Part B: Methodological 60(C):1–15.Crossref, Google Scholar
• Cui D, Curry D (2005) Prediction in marketing using the support vector machine. Marketing Sci. 24(4):595–615.Link, Google Scholar
• Dunning I, Huchette J, Lubin M (2017) JuMP: A modeling language for mathematical optimization. SIAM Rev. 59(2):295–320.Crossref, Google Scholar
• Dzyabura D, Hauser JR (2011) Active machine learning for consideration heuristics. Marketing Sci. 30(5):801–819.Link, Google Scholar
• Elrod T, Kumar SK (1993) Bias in the first choice rule for predicting share. Proc. 1998 Sawtooth Software Conf. (Sawtooth Software Company, Inc, Sun Valley, ID), 259–271.Google Scholar
• Evgeniou T, Boussios C, Zacharia G (2005) Generalized robust conjoint estimation. Marketing Sci. 24(3):415–429.Link, Google Scholar
• Evgeniou T, Pontil M, Toubia O (2007) A convex optimization approach to modeling consumer heterogeneity in conjoint estimation. Marketing Sci. 26(6):805–818.Link, Google Scholar
• Gelman A, Carlin J, Stern H, Dunson D, Vehtari A, Rubin D (2013) Bayesian Data Analysis, 3rd ed., Chapman & Hall/CRC Texts in Statistical Science (Taylor & Francis, Abingdon, UK).Crossref, Google Scholar
• Green PE, Srinivasan V (1978) Conjoint analysis in consumer research: Issues and outlook. J. Consumer Res. 5(2):103–123.Crossref, Google Scholar
• Grötschel M, Lovasz L, Schrijver A (2011) Geometric Algorithms and Combinatorial Optimization, Algorithms and Combinatorics (Springer, Berlin).Google Scholar
• Harville DA (2000) Matrix Algebra from a Statistician’s Perspective (Springer, Berlin).Google Scholar
• Hauser J, Toubia O (2005) The impact of utility balance and endogeneity in conjoint analysis. Marketing Sci. 24(3):498–507.Link, Google Scholar
• Huang D, Luo L (2016) Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Sci. 35(3):445–464.Link, Google Scholar
• Huber J, Zwerina K (1996) The importance of utility balance in efficient choice designs. J. Marketing Res. 33(3):307–317.Crossref, Google Scholar
• Huber J, Fiedler J, Miller R (1993) The effectiveness of alternative preference procedures in predicting choice. J. Marketing Res. 30(1):105–114.Crossref, Google Scholar
• Huchette J (2017) PiecewiseLinearOpt.jl. Accessed August 15, 2017, https://github.com/joehuchette/PiecewiseLinearOpt.jl.Google Scholar
• Huchette J, Vielma JP (2017) Nonconvex piecewise linear functions: Advanced formulations and simple modeling tools. Working paper, Massachusetts Institute of Technology, Cambridge, MA. arXiv:1708.00050.Google Scholar
• IBM ILOG (2018) CPLEX Optimizer. Accessed December 31, 2018, https://www.ibm.com/analytics/cplex-optimizer.Google Scholar
• Kanninen BJ (2002) Optimal design for multinomial choice experiments. J. Marketing Res. 39(2):214–227.Crossref, Google Scholar
• Kessels R, Jones B, Goos P, Vandebroek M (2009) An efficient algorithm for constructing bayesian optimal choice designs. J. Bus. Economi. Statist. 27(2):279–291.Crossref, Google Scholar
• Kuhfeld W, Tobias R, Garrat M (1994) Efficient experimental design with marketing research applications. J. Marketing Res. 31(4):545–575.Crossref, Google Scholar
• Liu Q, Tang Y (2015) Construction of heterogeneous conjoint choice designs: A new approach. Marketing Sci. 34(3):346–366.Link, Google Scholar
• Louviere JJ, Hensher DA, Swait JD (2000) Stated Choice Methods: Analysis and Application (Cambridge University Press, New York).Crossref, Google Scholar
• Lubin M, Dunning I (2015) Computing in operations research using julia. INFORMS J. Comput. 27(2):238–248.Link, Google Scholar
• McFadden D (1974) Conditional logit analysis of qualitative choice behaviour. Zarembka P, ed. Frontiers in Econometrics (Academic Press, New York), 105–142.Google Scholar
• Powell WB (2007) Approximate Dynamic Programming: Solving the Curses of Dimensionality, Wiley Series in Probability and Statistics (Wiley-Interscience, Hoboken, NJ).Crossref, Google Scholar
• QuadGK.jl (2017) Adaptive 1d numerical Gauss–Kronrod integration in Julia. Accessed August 15, 2017, https://github.com/JuliaMath/QuadGK.jl.Google Scholar
• Sándor Z, Wedel M (2001) Designing conjoint choice experiments using managers’ prior beliefs. J. Marketing Res. 38(4):430–444.Crossref, Google Scholar
• Sándor Z, Wedel M (2005) Heterogeneous conjoint choice designs. J. Marketing Res. 42(2):210–218.Crossref, Google Scholar
• Sauré D, Vielma JP (2017) Source code for “Ellipsoidal methods for adaptive choice-based conjoint analysis. Accessed November 19, 2017, https://github.com/juan-pablo-vielma/EMFACBCA.Google Scholar
• Segall DO (2010) Principles of multidimensional adaptive testing. van der Linden WJ, Glas CAW, eds. Elements of Adaptive Testing, Statistics for Social and Behavioral Sciences (Springer, New York), 57–75.Google Scholar
• Silvey S (1980) Optimal Design: An Introduction to the Theory for Parameter Estimation (Springer, New York).Crossref, Google Scholar
• Smith BJ (2017) Mamba: Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia. Accessed August 15, 2017, https://github.com/brian-j-smith/Mamba.jl.Google Scholar
• Sonnevend G (1986) An “analytical centre” for polyhedrons and new classes of global algorithms for linear (smooth, convex) programming. Prékopa A, Szelezsáan J, Strazicky B, eds. System Modelling Optimization, Lecture Notes in Control and Information Sciences, vol. 84 (Springer, Berlin), 866–875.Crossref, Google Scholar
• Stan Development Team (2017) Stan.jl: The julia interface to stan. Accessed August 15, 2017, https://github.com/goedman/Stan.jl.Google Scholar
• Toubia O, Hauser J, Garcia R (2007) Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Sci. 26(5):596–610.Link, Google Scholar
• Toubia O, Hauser JR, Simester DI (2004) Polyhedral methods for adaptive choice-based conjoint analysis. J. Marketing Res. 41(1):116–131.Crossref, Google Scholar
• Toubia O, Johnson E, Evgeniou T, Delquié P (2013) Dynamic experiments for estimating preferences: An adaptive method of eliciting time and risk parameters. Management Sci. 59(3):613–640.Link, Google Scholar
• Vielma JP, Ahmed S, Nemhauser G (2010) Mixed-integer models for nonseparable piecewise linear optimization: Unifying framework and extensions. Oper. Res. 58(2):303–315.Link, Google Scholar
• Wlömert N, Eggers F (2016) Predicting new service adoption with conjoint analysis: External validity of bdm-based incentive-aligned and dual-response choice designs. Marketing Lett. 27(1):195–210.Crossref, Google Scholar
• Yu J, Goss P, Vanderbroek M (2009) Efficient conjoint choice designs in the presence of respondent heterogeneity. Marketing Sci. 28(1):122–135.Link, Google Scholar
• Yu J, Goos P, Vandebroek M (2011) Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity. Internat. J. Res. Marketing 28(4):378–388.Crossref, Google Scholar
• Yu J, Goos P, Vandebroek M (2012) A comparison of different bayesian design criteria for setting up stated preference studies. Transportation Res. Part B: Methodological 46(7):789–807.Crossref, Google Scholar