Likelihood estimation of consumer preferences in choice-based conjoint analysis

European Journal of Operational Research

Volumn 239, Issue 2, 2014, Pages 556-564

  Halme, Merja ^a   Kallio, Markku ^a  

^a HELSINKI SCHOOL OF ECONOMICS   (Finland)

Author keywords

Conjoint analysis; Marketing research; Maximum likelihood; Preference estimation; Utility function

Indexed keywords

COMMERCE; MARKETING; MAXIMUM LIKELIHOOD; RESEARCH;

CHOICE-BASED CONJOINT; CONJOINT ANALYSIS; INDIVIDUAL PREFERENCE; LIKELIHOOD ESTIMATION; MARKETING RESEARCH; PERFORMANCE COMPARISON; PREDICTIVE PERFORMANCE; UTILITY FUNCTIONS;

STATISTICAL METHODS;

EID: 84904625933     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2014.05.044     Document Type: Article

References (16)

1. G.E. Allenby, and J.L. Ginter Using extremes to design products and segment markets Journal of Marketing Research 32 1995 392 403
2. S. Asmussen, and P. Glynn A new proof of convergence of MCMC via the ergodic theorem Statistics and Probability Letters 81 2011 1482 1485
3. S. Chib, and E. Greenberg Understanding Metropolis-Hastings algorithm The American Statistician 49 1995 327 335
4. Chrzan, K., & Orme, B. (2000). An overview and comparison of design strategies for choice-based conjoint analysis. < http://www.sawtoothsoftware. com/download/techpap/desgncbc.pdf > (Accessed 16.12.11).
5. W.S. DeSarbo, V. Ramaswamy, and S.H. Cohen Market segmentation with choice-based conjoint analysis Marketing Letters 6 1995 137 147
6. T. Evgeniou, M. Pontil, and O. Toubia A convex optimization approach to modeling consumer heterogeneity in conjoint estimation Marketing Science 26 2007 805 818
7. R. Fourer, D. Gay, and B.W. Kernighan AMPL 2nd ed. A modeling language for mathematical programming 2003 Brooks/Cole - Thomson Learning
8. S. Gensler, O. Hinz, B. Skiera, and S. Theysohn Willingness-to-pay estimation with choice-based conjoint analysis: Addressing extreme response behavior with individually adapted designs European Journal of Operational Research 219 2012 368 378
9. M. Halme, and M. Kallio Estimation methods for choice-based conjoint analysis of consumer preferences European Journal of Operational Research 214 2011 160 167
10. W. Hastings Monte Carlo sampling methods using Markov chains and their applications Biometrica 57 1970 97 109
11. E.V. Karniouchina, W.L. Moore, B. van der Rhee, and R. Verma Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research European Journal of Operational Research 197 2008 340 348
12. D. McFadden Conditional logit analysis of qualitative choice behavior P. Zarembka, Frontiers in econometrics 1974 Academic Press New York 105 142
13. N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller Equations of state calculations by fast computing machines Journal of Chemical Physics 21 1953 1087 1092
14. Nair, V. (2013). Fractional factorial design - A tutorial. < http://www.stat.lsa.umich.edu/samurphy/nida/seminars/ffd-tutorial.ppt > (Accessed 10.10.13).
15. M. Natter, and M. Feurstein Real world performance of choice-based conjoint models European Journal of Operational Research 137 2002 448 458
16. The HB / CBC System for Hierarchical Bayes estimation (2009). < http://www.sawtoothsoftware.com/support/technical-papers/hierarchical-bayes- estimation/cbc-hb-technical-paper-2009 > (Accessed 10.10.13).