View : 12 Download: 0

Bayesian inference and model selection in latent class logit models with parameter constraints: An application to market segmentation

Title
Bayesian inference and model selection in latent class logit models with parameter constraints: An application to market segmentation
Authors
Oh M.-S.Choi J.W.Kim D.-G.
Ewha Authors
오만숙
SCOPUS Author ID
오만숙scopus
Issue Date
2003
Journal Title
Journal of Applied Statistics
ISSN
0266-4763JCR Link
Citation
vol. 30, no. 2, pp. 191 - 204
Indexed
SCIE; SCOPUS WOS scopus
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.
DOI
10.1080/0266476022000023749
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE