Optimization-Based and Machine-Learning Methods for Conjoint Analysis: Estimation and Question Design
暂无分享,去创建一个
John R. Hauser | Olivier Toubia | Theodoros Evgeniou | T. Evgeniou | Olivier Toubia | J. Hauser | John R. Hauser | Theodoros Evgeniou Insead
[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 .