Maximum likelihood based sparse and distributed conjoint analysis

Efthymios Tsakonas, Joakim Jalden, Nicholas D. Sidiropoulos, Bjorn Ottersten

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A new statistical model for choice-based conjoint analysis is proposed. The model uses auxiliary variables to account for outliers and to detect the salient features that influence decisions. Unlike recent classification-based approaches to choice-based conjoint analysis, a sparsity-aware maximum likelihood (ML) formulation is proposed to estimate the model parameters. The proposed approach is conceptually appealing, mathematically tractable, and is also well-suited for distributed implementation. Its performance is tested and compared to the prior state-of-art using synthetic as well as real data coming from a conjoint choice experiment for coffee makers, with very promising results.

Original languageEnglish (US)
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages33-36
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Publication series

Name2012 IEEE Statistical Signal Processing Workshop, SSP 2012

Other

Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
CountryUnited States
CityAnn Arbor, MI
Period8/5/128/8/12

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    Tsakonas, E., Jalden, J., Sidiropoulos, N. D., & Ottersten, B. (2012). Maximum likelihood based sparse and distributed conjoint analysis. In 2012 IEEE Statistical Signal Processing Workshop, SSP 2012 (pp. 33-36). [6319698] (2012 IEEE Statistical Signal Processing Workshop, SSP 2012). https://doi.org/10.1109/SSP.2012.6319698