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Maximum likelihood based sparse and distributed conjoint analysis
Tsakonas, Efthymios; Jalden, Joakim; Sidiropoulos, Nicolas D. et al.
2012In Statistical Signal Processing Workshop (SSP), 2012 IEEE
Peer reviewed
 

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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.
Disciplines :
Computer science
Electrical & electronics engineering
Identifiers :
UNILU:UL-CONFERENCE-2012-500
Author, co-author :
Tsakonas, Efthymios
Jalden, Joakim
Sidiropoulos, Nicolas D.
Ottersten, Björn ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Language :
English
Title :
Maximum likelihood based sparse and distributed conjoint analysis
Publication date :
2012
Event name :
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Event place :
Ann Arbor, MI, United States
Event date :
5-8 August, 2012
Audience :
International
Main work title :
Statistical Signal Processing Workshop (SSP), 2012 IEEE
Publisher :
IEEE
ISBN/EAN :
978-1-4673-0182-4
Pages :
33 - 36
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 03 October 2013

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