Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/SSP.2012.6319698 | Statistical Signal Processing Workshop |
Keywords | DocType | ISSN |
maximum likelihood estimation,auxiliary variables,choice-based CA,choice-based conjoint analysis,classification-based approaches,maximum likelihood based distributed conjoint analysis,maximum likelihood based sparse conjoint analysis,model parameter estimation,sparsity-aware ML formulation,sparsity-aware maximum likelihood formulation | Conference | pending E-ISBN : 978-1-4673-0181-7 |
ISBN | Citations | PageRank |
978-1-4673-0181-7 | 2 | 0.43 |
References | Authors | |
3 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Efthymios Tsakonas | 1 | 15 | 3.50 |
| Joakim Jalden | 2 | 243 | 21.59 |
| N. D. Sidiropoulos | 3 | 930 | 69.06 |
| Björn E. Ottersten | 4 | 6418 | 575.28 |