Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 오만숙 | - |
dc.date.accessioned | 2016-08-28T11:08:12Z | - |
dc.date.available | 2016-08-28T11:08:12Z | - |
dc.date.issued | 2003 | - |
dc.identifier.issn | 0266-4763 | - |
dc.identifier.other | OAK-1318 | - |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/219138 | - |
dc.description.abstract | Latent class models have recently drawn considerable attention among many researchers and practitioners as a class of useful tools for capturing heterogeneity across different segments in a target market or population. In this paper, we consider a latent class logit model with parameter constraints and deal with two important issues in the latent class models - parameter estimation and selection of an appropriate number of classes - within a Bayesian framework. A simple Gibbs sampling algorithm is proposed for sample generation from the posterior distribution of unknown parameters. Using the Gibbs output, we propose a method for determining an appropriate number of the latent classes. A real-world marketing example as an application for market segmentation is provided to illustrate the proposed method. | - |
dc.language | English | - |
dc.title | Bayesian inference and model selection in latent class logit models with parameter constraints: An application to market segmentation | - |
dc.type | Article | - |
dc.relation.issue | 2 | - |
dc.relation.volume | 30 | - |
dc.relation.index | SCIE | - |
dc.relation.index | SCOPUS | - |
dc.relation.startpage | 191 | - |
dc.relation.lastpage | 204 | - |
dc.relation.journaltitle | Journal of Applied Statistics | - |
dc.identifier.doi | 10.1080/0266476022000023749 | - |
dc.identifier.wosid | WOS:000180623500005 | - |
dc.identifier.scopusid | 2-s2.0-0037306459 | - |
dc.author.google | Oh M.-S. | - |
dc.author.google | Choi J.W. | - |
dc.author.google | Kim D.-G. | - |
dc.contributor.scopusid | 오만숙(7201600334) | - |
dc.date.modifydate | 20230411111253 | - |