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March 2013 Spatio-Temporal Modeling of Legislation and Votes
Eric Wang, Esther Salazar, David Dunson, Lawrence Carin
Bayesian Anal. 8(1): 233-268 (March 2013). DOI: 10.1214/13-BA810

Abstract

A model is presented for analysis of multivariate binary data with spatio-temporal dependencies, and applied to congressional roll call data from the United States House of Representatives and Senate. The model considers each legislator’s constituency (location), the congressional session (time) of each vote, and the details (text) of each piece of legislation. The model can predict votes of new legislation from only text, while imposing smooth temporal evolution of legislator latent features, and correlation of legislators with adjacent constituencies. Additionally, the model estimates the number of latent dimensions required to represent the data. A Gibbs sampler is developed for posterior inference. The model is demonstrated as an exploratory tool of legislation and it performs well in quantitative comparisons to a traditional ideal-point model.

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Eric Wang. Esther Salazar. David Dunson. Lawrence Carin. "Spatio-Temporal Modeling of Legislation and Votes." Bayesian Anal. 8 (1) 233 - 268, March 2013. https://doi.org/10.1214/13-BA810

Information

Published: March 2013
First available in Project Euclid: 4 March 2013

zbMATH: 1329.62477
MathSciNet: MR3036261
Digital Object Identifier: 10.1214/13-BA810

Rights: Copyright © 2013 International Society for Bayesian Analysis

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Vol.8 • No. 1 • March 2013
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