Spatio-temporal modeling of legislation and votes

Bayesian Analysis

Volumn 8, Issue 1, 2013, Pages 233-268

  Wang, Eric ^a   Salazar, Esther ^b   Dunson, David ^c   Carin, Lawrence ^b  

^a UNITED STATES NAVAL ACADEMY   (United States)

^b Duke University   (United States)

^c DUKE UNIVERSITY   (United States)

Author keywords

Factor analysis; Indian buffet process; Latent Dirichlet allocation; Political science; Topic modeling

Indexed keywords

EID: 84875105360     PISSN: 19360975     EISSN: 19316690     Source Type: Journal    
DOI: 10.1214/13-BA810     Document Type: Article

References (30)

1. Ahmed, A. and Xing, E. P. (2010). "Staying Informed: Supervised and Semi-Supervised Multi-view Topical Analysis of Ideological Perspective." In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 1140-1150.
2. Blei, D. and Lafferty, J. (2006). "Dynamic Topic Models." In Proceedings of the 23rd International Conference on Machine Learning, 113-120.
3. Blei, D., Ng, A., and Jordan, M. (2003). "Latent Dirichlet Allocation." Journal of Machine Learning Research, 3: 993-1022.
4. Carvalho, C. M., Massam, H., and West, M. (2007). "Simulation of hyper-inverse Wishart distributions in graphical models." Biometrika, 94: 647-659.
5. Chib, S. and Greenberg, E. (1998). "Analysis of multivariate probit models." Biometrika, 85(2): 347-361.
6. Clinton, J., Jackman, D., and Rivers, S. (2004a). "The most liberal senator? Analyzing and interpreting Congressional Roll Calls." Political Science and Politics, 37(4): 805-811.
7. Clinton, J., Jackman, S., and Rivers, D. (2004b). "The Statistical Analysis of Roll Call Voting: A Uniffed Approach." American Political Science Review, 98(2): 355-370.
8. Clinton, J. and Meirowitz, A. (2003). "Integrating voting theory and roll call analysis: A framework." Political Analysis, 11: 381-396.
9. Dawid, A. and Lauritzen, S. (1993). "Hyper-Markov laws in the statistical analysis of decomposable graphical models." The Annals of Statistics, 21(3): 1272-1317.
10. Deerwester, S., Dumais, S., Furnas, G., Landauer, T., and Harshman, R. (1990). "Indexing by Latent Semantic Analysis." Journal of the American Society for Information Science, 41(6): 391-407.
11. Dunson, D. and Park, J. (2008). "Kernel stick-breaking processes." Biometrika, 95(2): 307-323.
12. Ferguson, T. (1973). "A Bayesian analysis of some nonparametric problems." The Annals of Statistics, 1(2): 209-230.
13. Gerrish, S. and Blei, D. (2011). "Predicting legislative roll calls from text." In Proceed- ings of the 28th Annual International Conference on Machine Learning.
14. Griffths, T. and Ghahramani, Z. (2005). "Inffnite latent feature models and the Indian buffet process." In Advances in Neural Information Processing Systems 18, 475-482. MIT Press.
15. Grimmer, J. (2010). "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases." Political Analysis, 18(1): 1-35.
16. Grimmer, J. and Stewart, B. (to appear in 2013). "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts." Political Analysis.
17. Hahn, P., Carvalho, C., and Scott, J. (2012). "A sparse factor-analytic probit model for Congressional voting patterns." Journal of the Royal Statistical Society Series C (Applied Statistics), 61(4): 619-635.
18. Jackman, S. (2001). "Multidimensional analysis of roll call data via Bayesian simulation: Identiffcation, estimation, inference, and model checking." Political Analysis, 9(3): 227-241.
19. Jackman, S. (2009). Bayesian Analysis for the Social Sciences. Wiley Series in Probability and Statistics. Wiley.
20. Paisley, J. and Carin, L. (2009). "Nonparametric factor analysis with beta process priors." In Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, 777-784.
21. Poole, K. and Rosenthal, H. (1985). "A spatial model for legislative roll call analysis." American Journal of Political Science, 29(2): 357-384.
22. Poole, K. (1997). Congress: A Political-Economic History of Roll Call Voting. New York: Oxford University Press.
23. Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., and Welling, M. (2008). "Fast Collapsed Gibbs Sampling for Latent Dirichlet Allocation." In KDD08: Pro- ceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 569-577.
24. Pruteanu-Malinici, I., Ren, L., Paisley, J., Wang, E., and Carin, L. (2010). "Hierarchical Bayesian Modeling of Topics in Time-Stamped Documents." IEEE Transactions on Pattern Analysis Machine Intelligence, 32(6): 996-1011.
25. Quinn, K., Monroe, B., Colaresi, M., Crespin, M., and Radev, D. (2006). "An automated method of topic-coding legislative speech over time with application to the 105th- 108th U.S. senate." In Midwest Political Science Association Meeting, 1-61.
26. Salakhutdinov, R. and Mnih, A. (2008). "Probabilistic Matrix Factorization." In Advances in Neural Information Processing Systems 21, volume 20, 1257-1264. MIT Press.
27. Srebo, N., Rennie, J., and Jaakkola, T. (2005). "Maximum-Margin Matrix Factorization." In Advances in Neural Information Processing Systems 18, 1329-1336. MIT Press.
28. Steyvers, M. and Griffths, T. (2007). "Probabilistic topic models." Handbook of latent semantic analysis, 427(7): 424-440.
29. Wallach, H. (2008). "Structured Topic Models for Language." Ph.D. thesis, University of Cambridge.
30. Wang, E., Liu, D., Silva, J., Dunson, D., and Carin, L. (2010). "Joint Analysis of Time-Evolving Binary Matrices and Associated Documents." In Advances in Neural Information Processing Systems 23, 2370-2378. 2370-2378