A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data

Wayne Desarbo, Youngchan Kim, Duncan Fong

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

We present a new Bayesian formulation of a vector multidimensional scaling procedure for the spatial analysis of binary choice data. The Gibbs sampler is gainfully employed to estimate the posterior distribution of the specified scalar products, bilinear model parameters. The computational procedure allows for the explicit estimation of a covariance matrix which can accommodate violations of IIA due to context effects. In addition, posterior standard errors can be estimated which reflect differential degrees of consumer choice uncertainty and/or brand position instability. A marketing application concerning the analysis of consumers' consideration sets for luxury automobiles is provided to illustrate the use of the proposed methodology.

Original languageEnglish (US)
Pages (from-to)79-108
Number of pages30
JournalJournal of Econometrics
Volume89
Issue number1-2
DOIs
StatePublished - Nov 26 1998

Fingerprint

Spatial analysis
Multidimensional scaling
Luxury
Violations
Marketing
Consideration sets
Binary choice
Posterior distribution
Gibbs sampler
Automobile
Uncertainty
Context effects
Methodology
Covariance matrix
Consumer choice
Standard error

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

Cite this

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A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data. / Desarbo, Wayne; Kim, Youngchan; Fong, Duncan.

In: Journal of Econometrics, Vol. 89, No. 1-2, 26.11.1998, p. 79-108.

Research output: Contribution to journalArticle

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