Optimization-Based and Machine-Learning Methods for Conjoint Analysis: Estimation and Question Design

Soon after the introduction of conjoint analysis into marketing by Green and Rao (1972), Srinivasan and Shocker (1973a, 1973b) introduced a conjoint analysis estimation method, Linmap, based on linear programming. Linmap has been applied successfully in many situations and has proven to be a viable alternative to statistical estimation (Jain, et. al. 1979, Wittink and Cattin 1981). Recent modification to deal with “strict pairs” has improved the estimation accuracy with the result that, on occasion, the modified Linmap predicts holdout data better than statistical estimation based on hierarchical Bayes methods (Srinivasan 1998, Hauser, et. al. 2006).

[1]  John R. Hauser,et al.  Fast Polyhedral Adaptive Conjoint Estimation , 2002 .

[2]  Peter E. Rossi,et al.  Marketing models of consumer heterogeneity , 1998 .

[3]  Paul E. Green,et al.  Thirty Years of Conjoint Analysis: Reflections and Prospects , 2001, Interfaces.

[4]  Joel Huber,et al.  The Importance of Utility Balance in Efficient Choice Designs , 1996 .

[5]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[6]  Paul E. Green,et al.  Adaptive Conjoint Analysis: Some Caveats and Suggestions , 1991 .

[7]  S. Addelman Symmetrical and Asymmetrical Fractional Factorial Plans , 1962 .

[8]  J. Orlin,et al.  “ MUST HAVE ” ASPECTS VS . TRADEOFF ASPECTS IN MODELS OF CUSTOMER DECISIONS , 2006 .

[9]  W. Newey,et al.  Large sample estimation and hypothesis testing , 1986 .

[10]  Richard M. Johnson Comment on “Adaptive Conjoint Analysis: Some Caveats and Suggestions”7 , 1991 .

[11]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[12]  Olivier Toubia,et al.  The Impact of Utility Balance and Endogeneity in Conjoint Analysis , 2005 .

[13]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[14]  John R. Hauser,et al.  Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis , 2004 .

[15]  Vijay Mahajan,et al.  A Comparison of the Internal Validity of Alternative Parameter Estimation Methods in Decompositional Multiattribute Preference Models , 1979 .

[16]  M. Wedel,et al.  Designing Conjoint Choice Experiments Using Managers' Prior Beliefs , 2001 .

[17]  OU Wei-hua,et al.  Linear Model Selection by Cross-validation , 2009 .

[18]  R. Dawes,et al.  Linear models in decision making. , 1974 .

[19]  H. J. Einhorn The use of nonlinear, noncompensatory models in decision making. , 1970, Psychological bulletin.

[20]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[21]  Philippe Cattin,et al.  Alternative Estimation Methods for Conjoint Analysis: A Monté Carlo Study , 1981 .

[22]  Allan D. Shocker,et al.  Linear programming techniques for multidimensional analysis of preferences , 1973 .

[23]  D. M. Titterington,et al.  Recent advances in nonlinear experiment design , 1989 .

[24]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[25]  Giorgos Zacharia,et al.  Generalized robust conjoint estimation , 2005 .

[26]  Barbara Kanninen,et al.  Optimal Design for Multinomial Choice Experiments , 2002 .

[27]  William G. Cochran,et al.  Experimental Designs, 2nd Edition , 1950 .

[28]  Olivier Toubia,et al.  Eliciting Consumer Preferences Using Robust Adaptive Choice Questionnaires , 2008, IEEE Transactions on Knowledge and Data Engineering.

[29]  W. Greene,et al.  计量经济分析 = Econometric analysis , 2009 .

[30]  Daphne Koller,et al.  Support Vector Machine Active Learning with Application sto Text Classification , 2000, ICML.

[31]  P. Lenk,et al.  Hierarchical Bayes Conjoint Analysis: Recovery of Partworth Heterogeneity from Reduced Experimental Designs , 1996 .

[32]  Allan D. Shocker,et al.  Estimating the weights for multiple attributes in a composite criterion using pairwise judgments , 1973 .

[33]  K. Chaloner,et al.  Bayesian Experimental Design: A Review , 1995 .

[34]  Vithala R. Rao,et al.  Conjoint Measurement- for Quantifying Judgmental Data , 1971 .

[35]  Joel Huber,et al.  Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments , 2001 .

[36]  Peter E. Rossi,et al.  Bayesian Statistics and Marketing , 2005 .

[37]  M. Pontil,et al.  A Convex Optimization Approach to Modeling Consumer Heterogeneity in Conjoint Estimation , 2007 .

[38]  John R. Hauser,et al.  Probabilistic Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis: Theory and Application , 2007 .

[39]  Tomaso A. Poggio,et al.  Regularization Networks and Support Vector Machines , 2000, Adv. Comput. Math..

[40]  J. Shao Linear Model Selection by Cross-validation , 1993 .

[41]  Peter E. Rossi,et al.  Bayesian Statistics and Marketing: Rossi/Bayesian Statistics and Marketing , 2006 .

[42]  W. G. Hunter,et al.  Experimental Design: Review and Comment , 1984 .

[43]  L. Galway Spline Models for Observational Data , 1991 .

[44]  Mark J. Garratt,et al.  Efficient Experimental Design with Marketing Research Applications , 1994 .