Ottersten, Björn[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2012
Statistical Signal Processing Workshop (SSP), 2012 IEEE
IEEE
33 - 36
Yes
International
978-1-4673-0182-4
Statistical Signal Processing Workshop (SSP), 2012 IEEE
5-8 August, 2012
Ann Arbor, MI
USA
[en] 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.