Data Augmentation and MCMC for Binary and Multinomial Logit Models View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2009-12-29

AUTHORS

Sylvia Frühwirth-Schnatter , Rudolf Frühwirth

ABSTRACT

The paper introduces two new data augmentation algorithms for sampling the parameters of a binary or multinomial logit model from their posterior distribution within a Bayesian framework. The new samplers are based on rewriting the underlying random utility model in such away that only differences of utilities are involved. As a consequence, the error term in the logit model has a logistic distribution. If the logistic distribution is approximated by a finite scale mixture of normal distributions, auxiliary mixture sampling can be implemented to sample from the posterior of the regression parameters. Alternatively, a data augmented Metropolis–Hastings algorithm can be formulated by approximating the logistic distribution by a single normal distribution. A comparative study on five binomial and multinomial data sets shows that the new samplers are superior to other data augmentation samplers and to Metropolis–Hastings sampling without data augmentation. More... »

PAGES

111-132

Book

TITLE

Statistical Modelling and Regression Structures

ISBN

978-3-7908-2412-4
978-3-7908-2413-1

From Grant

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-7908-2413-1_7

DOI

http://dx.doi.org/10.1007/978-3-7908-2413-1_7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1008350313


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