Preference estimation under bounded rationality: identification of attribute non-attendance in stated-choice data using a support vector machines approach

DOI10.1016/j.ejor.2022.04.018OpenAlexW4224290921WikidataQ114184274 ScholiaQ114184274MaRDI QIDQ2171627

Ricardo Montoya, Sebastián Maldonado, Verónica Díaz

Publication date: 9 September 2022

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2022.04.018

zbMATH Keywords

machine learningOR in marketingattribute non-attendancechoice-based conjoint

Mathematics Subject Classification ID

Operations research and management science (90Bxx)

Cites Work

• Issues in the use of ratings-based versus choice-based conjoint analysis in operations management research
• Linear programming techniques for multidimensional analysis of preferences
• High dimensional data classification and feature selection using support vector machines
• Simultaneous feature selection and clustering based on square root optimization
• A novel embedded min-max approach for feature selection in nonlinear support vector machine classification
• Using hierarchical Bayes draws for improving shares of choice predictions in conjoint simulations: a study based on conjoint choice data
• Estimation methods for choice-based conjoint analysis of consumer preferences
• Advanced conjoint analysis using feature selection via support vector machines
• Discrete Choice Methods with Simulation
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