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Maximum likelihood based sparse and distributed conjoint analysis
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-6630-243X
University of Minessota.
KTH, School of Electrical Engineering (EES), Signal Processing. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0003-2298-6774
2012 (English)In: 2012 IEEE Statistical Signal Processing Workshop, SSP 2012, IEEE , 2012, 33-36 p.Conference paper, Published paper (Refereed)
Abstract [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.

Place, publisher, year, edition, pages
IEEE , 2012. 33-36 p.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-104178DOI: 10.1109/SSP.2012.6319698ISI: 000309943200009Scopus ID: 2-s2.0-84868220098ISBN: 978-1-4673-0183-1 (print)OAI: oai:DiVA.org:kth-104178DiVA: diva2:563303
Conference
2012 IEEE Statistical Signal Processing Workshop, SSP 2012;Ann Arbor, MI;5 August 2012 through 8 August 2012
Funder
EU, FP7, Seventh Framework Programme, 228044ICT - The Next Generation
Note

QC 20121123

Available from: 2012-10-30 Created: 2012-10-29 Last updated: 2013-04-15Bibliographically approved

Open Access in DiVA

fulltext(150 kB)269 downloads
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Jaldén, JoakimOttersten, Björn

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