A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data
type: Item
instance of: scholarly article
title: A Bayesian multidimensional scaling procedure for the spatial analysis of revealed choice data
zbMATH Open document ID: 0958.62112
DOI: 10.1016/S0304-4076(98)00056-6
author: Youngchan Kim
author: Duncan Fong
author: Wayne S. DeSarbo
published in: Journal of Econometrics
publication date: 2001-04-08T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 91B26
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 91C15
zbMATH DE Number: 1343108
zbMATH Keywords: choice models
zbMATH Keywords: market structure analysis
zbMATH Keywords: context effects
zbMATH Keywords: consideration sets
zbMATH Keywords: multidimensional scaling
describes a project that uses: BayesDA
MaRDI profile type: Publication
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full work available at URL: s0304-4076(98)00056-6
OpenAlex ID: W2114659646
description: scientific article; zbMATH DE number 1343108