Maximum likelihood based sparse and distributed conjoint analysis
2012 (English)In: 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, IEEE , 2012, 33-36 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2012. 33-36 p.
IdentifiersURN: urn:nbn:se:kth:diva-104178DOI: 10.1109/SSP.2012.6319698ISI: 000309943200009ScopusID: 2-s2.0-84868220098ISBN: 978-1-4673-0183-1OAI: oai:DiVA.org:kth-104178DiVA: diva2:563303
2012 IEEE Statistical Signal Processing Workshop, SSP 2012;Ann Arbor, MI;5 August 2012 through 8 August 2012
FunderEU, FP7, Seventh Framework Programme, 228044ICT - The Next Generation
QC 201211232012-10-302012-10-292013-04-15Bibliographically approved