Bayesian Inference in the Multinomial Logit Model

Frühwirth-Schnatter, Sylvia and Frühwirth, Rudolf (2012) Bayesian Inference in the Multinomial Logit Model. Austrian Journal of Statistics, 41 (1). pp. 27-43. ISSN 1026-597X

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Abstract

The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.

Item Type: Article
Keywords: Bayesian Inference, Finite Mixture Distributions, Markov Chain Monte Carlo, Metropolis-Hastings Algorithm, Multinomial Logit Model, Multivariate Logistic Distribution.
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Published
Variance from Published Version: None
Depositing User: ePub Administrator
Date Deposited: 10 Jul 2017 12:34
Last Modified: 15 Nov 2017 02:23
Related URLs:
FIDES Link: https://bach.wu.ac.at/d/research/results/56838/
URI: https://epub.wu.ac.at/id/eprint/5629

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