Abstract
The wandering ideal point (WIP) model developed by De Soete, Carroll and DeSarbo in 1986 has been used to analyze individual preferences data. Usually the marginal maximum likelihood (MML) method is applied to estimate the subject’s ideal points in the WIP model. However, each ideal point estimated by the Bayes expected a posteriori estimation procedure in the MML method is different in nature from that which should be estimated as a mean vector of the multivariate normal distribution each independently assigned to its respective subject point according to the original WIP model. In this paper I take a Bayesian approach to estimate the distribution of each ideal point. Explicitly, the Gibbs sampler, one of the Marcov Chain Monte Carlo methods, is implemented for fitting the WIP model to “pick any/n” type of binary data. Three applications are provided which demonstrate that the method is useful to estimate the distribution of each subject point.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.