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Analysis of space-time relational data with application to legislative voting

Publication ,  Journal Article
Salazar, E; Dunson, DB; Carin, L
Published in: Computational Statistics and Data Analysis
July 29, 2013

We consider modeling spatio-temporally indexed relational data, motivated by analysis of voting data for the United States House of Representatives over two decades. The data are characterized by incomplete binary matrices, representing votes of legislators on legislation over time. The spatial covariates correspond to the location of a legislator's district, and time corresponds to the year of a vote. We seek to infer latent features associated with legislators and legislation, incorporating spatio-temporal structure. A model of such data must impose a flexible representation of the space-time structure, since the apportionment of House seats and the total number of legislators change over time. There are 435 congressional districts, with one legislator at a time for each district; however, the total number of legislators typically changes from year to year, for example due to deaths. A matrix kernel stick-breaking process (MKSBP) is proposed, with the model employed within a probit-regression construction. Theoretical properties of the model are discussed and posterior inference is developed using Markov chain Monte Carlo methods. Advantages over benchmark models are shown in terms of vote prediction and treatment of missing data. Marked improvements in results are observed based on leveraging spatial (geographical) information. © 2013 Elsevier B.V. All rights reserved.

Duke Scholars

Published In

Computational Statistics and Data Analysis

DOI

ISSN

0167-9473

Publication Date

July 29, 2013

Volume

68

Start / End Page

141 / 154

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0802 Computation Theory and Mathematics
  • 0104 Statistics
 

Citation

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Salazar, E., Dunson, D. B., & Carin, L. (2013). Analysis of space-time relational data with application to legislative voting. Computational Statistics and Data Analysis, 68, 141–154. https://doi.org/10.1016/j.csda.2013.06.018
Salazar, E., D. B. Dunson, and L. Carin. “Analysis of space-time relational data with application to legislative voting.” Computational Statistics and Data Analysis 68 (July 29, 2013): 141–54. https://doi.org/10.1016/j.csda.2013.06.018.
Salazar E, Dunson DB, Carin L. Analysis of space-time relational data with application to legislative voting. Computational Statistics and Data Analysis. 2013 Jul 29;68:141–54.
Salazar, E., et al. “Analysis of space-time relational data with application to legislative voting.” Computational Statistics and Data Analysis, vol. 68, July 2013, pp. 141–54. Scopus, doi:10.1016/j.csda.2013.06.018.
Salazar E, Dunson DB, Carin L. Analysis of space-time relational data with application to legislative voting. Computational Statistics and Data Analysis. 2013 Jul 29;68:141–154.
Journal cover image

Published In

Computational Statistics and Data Analysis

DOI

ISSN

0167-9473

Publication Date

July 29, 2013

Volume

68

Start / End Page

141 / 154

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0802 Computation Theory and Mathematics
  • 0104 Statistics