Quotation Frühwirth-Schnatter, Sylvia, Frühwirth, Rudolf. 2007. Auxiliary mixture sampling with applications to logistic models. Computational Statistics & Data Analysis 51 (7): 3509-3528.


RIS


BibTeX

Abstract

A new method of data augmentation for binary and multinomial logit models is described. First, the latent utilities are introduced as auxiliary latent variables, leading to a latent model which is linear in the unknown parameters, but involves errors from the type I extreme value distribution. Second, for each error term the density of this distribution is approximated by a mixture of nortmal distributions, and the component indicators in these mixtures are introduced as further latent variables. This leads to Markov chain Monte Carlo estimation based on a convenient auxiliary mixture sampler that drasw from standard distributions like normal or exponential distributions and, in contrast to more common Metropolis-Hastings approaches, does not require any tuning. It is shown how the auxiliary mixture sampler is implemented for binary or multinomial logit models, and it is demonstrated how to extend the sampler to mixed effect models and time-varying parameter models for binary and categorical data. Finally, an application to Austrian labor market data is discussed.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Computational Statistics and Data Analysis
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-D, WH-B
Language English
Title Auxiliary mixture sampling with applications to logistic models
Volume 51
Number 7
Year 2007
Page from 3509
Page to 3528
URL http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235880%232007%23999489992%23646939%23FLA%23&_cdi=5880&_pubType=J&_auth=y&_acct=C000022138&_version=1&_urlVersion=0&_userid=464393&md5=c0961390f80cdc462044e65dee902838

Associations

People
Frühwirth-Schnatter, Sylvia (Details)
Frühwirth, Rudolf (Former researcher)
Organization
Institute for Statistics and Mathematics IN (Details)
Google Scholar: Search