dc.contributorSosa Martinez, Juan Camilo
dc.contributorUniversidad Santo Tomás
dc.creatorLuque Zabala, Carolina Maria
dc.date.accessioned2021-09-22T17:13:55Z
dc.date.available2021-09-22T17:13:55Z
dc.date.created2021-09-22T17:13:55Z
dc.date.issued2021-09-16
dc.identifierLuque, C. (2021). Métodos Bayesianos para caracterizar el comportamiento legislativo del Senado colombiano en el periodo 2010–2014 (Tesis de maestría). Universidad Santo Tomás, Bogotá, Colombia.
dc.identifierhttp://hdl.handle.net/11634/35664
dc.identifierreponame:Repositorio Institucional Universidad Santo Tomás
dc.identifierinstname:Universidad Santo Tomás
dc.identifierrepourl:https://repository.usta.edu.co
dc.description.abstractThis work applies Bayesian methodologies to characterize the legislative behavior of the Colombian Senate during the 2010–2014 period. The analysis is done through the plenary roll-call votes of this legislative chamber. Furthermore, parliamentary electoral behavior is operationalized by implementing the one-dimensional standard Bayesian ideal point estimator by means of Markov chain Monte Carlo algorithms. The results mainly provide contributions in two directions: Political space dimensionality and pivotal legislators identification. Patterns revealed by the estimated ideal points suggest non–ideological latent trait (opposition–no opposition) underlying the vote of the Senate deputies. Thus, in addition to providing empirical evidence for a better understanding of legislative policy in Colombia during the period under analysis, this work also offers methodological, theoretical, and practical tools to guide the pre-processing and analysis of roll-call data in contexts of unbalanced parliaments (as opposed to the North American parliament), taking as a reference the particular case of the Colombian’s Senate.
dc.languagespa
dc.publisherUniversidad Santo Tomás
dc.publisherMaestría Estadística Aplicada
dc.publisherFacultad de Estadística
dc.relationAlbert, J. H. (1992). Bayesian estimation of normal ogive item response curves using gibbs sampling. Journal of educational statistics, 17(3):251–269.
dc.relationAlbert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polychotomous response data. Journal of the American statistical Association, 88(422):669–679.
dc.relationAldrich, J. H., Montgomery, J. M., and Sparks, D. B. (2014). Polarization and ideology: Partisan sources of low dimensionality in scaled roll call analyses. Political Analysis, pages 435–456.
dc.relationAlemán, E. (2008). Policy positions in the chilean senate: An analysis of coauthorship and roll call data. Brazilian Political Science Review (Online), 3(SE):0–0.
dc.relationAlemán, E., Calvo, E., Jones, M. P., and Kaplan, N. (2009). Comparing cosponsorship and roll-call ideal points. Legislative Studies Quarterly, 34(1):87–116.
dc.relationAlemán, E., Micozzi, J. P., Pinto, P. M., and Saiegh, S. (2018). Disentangling the role of ideology and partisanship in legislative voting: evidence from argentina. Legislative Studies Quarterly, 43(2):245– 273.
dc.relationAlemán, E. and Navia, P. (2016). Presidential power, legislative rules, and lawmaking in Chile. Legislative Institutions and Lawmaking in Latin America, pages 92–121.
dc.relationAlemán, E. and Pachón, M. (2008). Las comisiones de conciliación en los procesos legislativos de Chile y Colombia. Política y gobierno, 15(1):03–34.
dc.relationAlston, L. J. and Mueller, B. (2006). Pork for policy: executive and legislative exchange in Brazil. Journal of Law, Economics, and Organization, 22(1):87–114.
dc.relationAmemiya, T. (1984). Tobit models: A survey. Journal of econometrics, 24(1-2):3–61.
dc.relationAmes, B. (2002). Party discipline in the chamber of deputies. Legislative Politics in Latin America, pages 185–221.
dc.relationArcher, R. P. and Shugart, M. S. (1997). The unrealized potential of presidential dominance in Colombia. Presidentialism and democracy in Latin America, pages 110–160.
dc.relationArrow, K. (1990). Advances in the spatial theory of voting. Cambridge University Press.
dc.relationAsmussen, N. and Jo, J. (2016). Anchors away: a new approach for estimating ideal points comparable across time and chambers. Political Analysis, pages 172–188.
dc.relationAumann, R. (1964). The bargaining set for cooperative games. M. Dresher, LS Shapley and A. W. Tucker, eds., gdvances in game theory. Princeton, NJ: Princeton University Press, pp. AA3-A76.
dc.relationBailey, M. A., Strezhnev, A., and Voeten, E. (2017). Estimating dynamic state preferences from United Nations voting data. Journal of Conflict Resolution, 61(2):430–456.
dc.relationBarberá, P. (2015). Birds of the same feather tweet together: Bayesian ideal point estimation using twitter data. Political analysis, 23(1):76–91.
dc.relationBayarri, M. and Berger, J. O. (2000). P values for composite null models. Journal of the American Statistical Association, 95(452):1127–1142.
dc.relationBeal, M. J. (2003). Variational algorithms for approximate Bayesian inference. University of London, University College London (United Kingdom).
dc.relationBenoit, K. and Laver, M. (2012). The dimensionality of political space: Epistemological and methodolo gical considerations. European Union Politics, 13(2):194–218.
dc.relationBerger, J. O. (2013). Statistical decision theory and Bayesian analysis. Chapter 3. Prior Information and Subjective Probability. Springer Science and Business Media.
dc.relationBernabel, R. (2015). Does the electoral rule matter for political polarization? the case of brazilian legislative chambers. Brazilian Political Science Review, 9(2):81–108.
dc.relationBernardo, J., Bayarri, M., Berger, J., Dawid, A., Heckerman, D., Smith, A., and West, M. (2003). Bayesian factor regression models in the “large p, small n” paradigm. Bayesian statistics, 7:733–742.
dc.relationBetancourt, M. (2017). A conceptual introduction to Hamiltonian Monte Carlo. arXiv preprint ar Xiv:1701.02434.
dc.relationBetancourt, M. (2019). The convergence of Markov Chain Monte Carlo methods: from the Metropolis method to Hamiltonian Monte Carlo. Annalen der Physik, 531(3):1700214.
dc.relationBlack, D. et al. (1958). The theory of committees and elections. Springer.
dc.relationBlei, D. M., Kucukelbir, A., and McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American statistical Association, 112(518):859–877.
dc.relationBorges, A., Turgeon, M., and Albala, A. (2020). Electoral incentives to coalition formation in multiparty presidential systems. Party Politics, page 1-11.
dc.relationBradley, I. and Meek, R. L. (2014). Matrices and society: matrix algebra and its applications in the social sciences. Princeton University Press.
dc.relationBrazill, T. J. and Grofman, B. (2002). Factor analysis versus multi-dimensional scaling: binary choice roll-call voting and the us supreme court. Social Networks, 24(3):201–229.
dc.relationBrodersen, K. H., Daunizeau, J., Mathys, C., Chumbley, J. R., Buhmann, J. M., and Stephan, K. E. (2013). Variational Bayesian mixed-effects inference for classification studies. Neuroimage, 76:345–361.
dc.relationCahoon, L., Hinich, M. J., and Ordeshook, P. C. (1978). A statistical multidimensional scaling method based on the spatial theory of voting. In Graphical representation of multivariate data, pages 243–278. Elsevier.
dc.relationCárdenas, M., Junguito, R., and Pachón, M. (2008). Political institutions and policy outcomes in Colombia: The effects of the 1991 constitution. Policymaking in Latin America: how politics shapes policies, pages 199–242.
dc.relationCarey, J. M. (1998). Parties, Coalitions, and the Chilean Congress in the 1990s. Latin American Studies Association.
dc.relationCarlin, B. P. and Louis, T. A. (2008). Bayesian methods for data analysis. CRC Press.
dc.relationCarpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P., and Riddell, A. (2017). Stan: a probabilistic programming language. Grantee Submission, 76(1):1–32.
dc.relationCarroll, R., Lewis, J. B., Lo, J., Poole, K. T., and Rosenthal, H. (2009a). Comparing NOMINATE and IDEAL: Points of difference and Monte Carlo tests. Legislative Studies Quarterly, 34(4):555–591.
dc.relationCarroll, R., Lewis, J. B., Lo, J., Poole, K. T., and Rosenthal, H. (2009b). Measuring bias and uncertainty in DW-NOMINATE ideal point estimates via the parametric bootstrap. Political analysis, pages 261– 275.
dc.relationCarroll, R., Lewis, J. B., Lo, J., Poole, K. T., and Rosenthal, H. (2013). The structure of utility in spatial models of voting. American Journal of Political Science, 57(4):1008–1028.
dc.relationCarroll, R. and Pach´on, M. (2016). The unrealized potential of presidential coalitions in Colombia. Legislative Institutions and Lawmaking in Latin America, pages 122–147.
dc.relationCarvalho, C. M., Chang, J., Lucas, J. E., Nevins, J. R., Wang, Q., and West, M. (2008). High-dimensional sparse factor modeling: applications in gene expression genomics. Journal of the American Statistical Association, 103(484):1438–1456.
dc.relationCastillo, I., Schmidt-Hieber, J., Van der Vaart, A., et al. (2015). Bayesian linear regression with sparse priors. Annals of Statistics, 43(5):1986–2018.
dc.relationCheibub, J. A., Figueiredo, A., and Limongi, F. (2009). Political parties and governors as determinants of legislative behavior in brazil’s chamber of deputies, 1988–2006. Latin American Politics and Society, 51(1):1–30.
dc.relationChib, S. and Greenberg, E. (1998). Analysis of multivariate probit models. Biometrika, 85(2):347–361.
dc.relationCifuentes-Silva, F., Rivera-Polo, F., Labra-Gayo, J. E., and Astudillo, H. (2021). Describing the nature of legislation through roll call voting in the chilean national congress, a linked dataset description. Semantic web.
dc.relationClerici, P. (2021). Legislative territorialization: The impact of a decentralized party system on individual legislative behavior in Argentina. Publius: The Journal of Federalism, 51(1):104–130.
dc.relationClinton, J., Jackman, S., and Rivers, D. (2004). The statistical analysis of roll call data. American Political Science Review, pages 355–370.
dc.relationClinton, J. D. and Jackman, S. (2009). To simulate or nominate? Legislative Studies Quarterly, 34(4):593– 621.
dc.relationClinton, J. D. and Meirowitz, A. (2001). Agenda constrained legislator ideal points and the spatial voting model. Political Analysis, pages 242–259.
dc.relationCohen, L. R. and Noll, R. G. (1991). How to vote, whether to vote: Strategies for voting and abstaining on congressional roll calls. Political Behavior, 13(2):97–127.
dc.relationColprensa (2013). El PIN cambio el nombre de su partido a Opción Ciudadana. El país.
dc.relationCongdon, P. (2007). Bayesian statistical modelling, volume 704. John Wiley & Sons.
dc.relationCoughlin, P. and Nitzan, S. (1981). Electoral outcomes with probabilistic voting and nash social welfare maxima. Journal of Public Economics, 15(1):113–121.
dc.relationCox, R. T. (1946). Probability, frequency and reasonable expectation. American journal of physics, 14(1):1–13.
dc.relationCox, R. T. (1963). The algebra of probable inference. American Journal of Physics, 31(1):66–67.
dc.relationCroux, C., Dhaene, G., and Hoorelbeke, D. (2004). Robust standard errors for robust estimators. CES Discussion paper series (DPS) 03.16, pages 1–20.
dc.relationDavis, O. A. and Hinich, M. J. (1965). A mathematical model of policy formation in a democratic society. Graduate School of Industrial Administration, Carnegie Institute of Technology.
dc.relationDavis, O. A., Hinich, M. J., and Ordeshook, P. C. (1970). An expository development of a mathematical model of the electoral process. The American Political Science Review, 64(2):426–448.
dc.relationDe la Horra, J. and Rodriguez-Bernal, M. T. (2001). Posterior predictive p-values: what they are and what they are not. Test, 10(1):75–86.
dc.relationDe Leeuw, J. (2006). Principal component analysis of binary data by iterated singular value decompo sition. Computational statistics & data analysis, 50(1):21–39.
dc.relationde Valpine, P., Turek, D., Paciorek, C. J., Anderson-Bergman, C., Lang, D. T., and Bodik, R. (2017). Programming with models: writing statistical algorithms for general model structures with NIMBLE. Journal of Computational and Graphical Statistics, 26(2):403–413.
dc.relationDe Vries, C. E. and Marks, G. (2012). The struggle over dimensionality: A note on theory and empirics. European Union Politics, 13(2):185–193.
dc.relationDenwood, M. J. (2016). runjags: An R package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. Journal of statistical software, 71(1):1–25.
dc.relationDesposato, S. W. (2001). Legislative politics in authoritarian brazil. Legislative Studies Quarterly, pages 287–317.
dc.relationDesposato, S. W. (2003). Comparing group and subgroup cohesion scores: A nonparametric method with an application to Brazil. Political Analysis, pages 275–288.
dc.relationDougherty, K. L., Lynch, M. S., and Madonna, A. J. (2014). Partisan agenda control and the dimensionality of congress. American Politics Research, 42(4):600–627.
dc.relationDowns, A. (1957). An economic theory of political action in a democracy. Journal of political economy, 65(2):135–150.
dc.relationEnelow, J. M. and Hinich, M. J. (1984). The spatial theory of voting: An introduction. CUP Archive.
dc.relationFigueiredo, A. C. and Limongi, F. (2000). Presidential power, legislative organization, and party behavior in Brazil. Comparative Politics, pages 151–170.
dc.relationFowler, J. H. (2006). Legislative cosponsorship networks in the us house and senate. Social Networks, 28(4):454–465.
dc.relationGamm, G. and Huber, J. (2002). Legislatures as political institutions: Beyond the contemporary congress. In IraKatznelson and Milner, H. V., editors, Political science: State of the discipline, pages 313–341. New York: W.W. Norton.
dc.relationGarthwaite, P. H., Kadane, J. B., and O’Hagan, A. (2005). Statistical methods for eliciting probability distributions. Journal of the American Statistical Association, 100(470):680–701.
dc.relationGelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2014). Bayesian data analysis. CRC press.
dc.relationGelman, A., Meng, X.-L., and Stern, H. (1996). Posterior predictive assessment of model fitness via realized discrepancies. Statistica sinica, pages 733–760.
dc.relationGelman, A., Rubin, D. B., et al. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 7(4):457–472.
dc.relationGerrish, S. and Blei, D. (2012). How they vote. Issue-adjusted models of legislative behavior. Advances in neural information processing systems, 25:2753–2761.
dc.relationGeweke, J. et al. (1991). Evaluating the accuracy of sampling-based approaches to the calculation of poste rior moments, volume 196. Federal Reserve Bank of Minneapolis, Research Department Minneapolis, MN.
dc.relationGeyer, C. J. (1992). Practical Markov Chain Monte Carlo. Statistical science, pages 473–483.
dc.relationGill, J. and Walker, L. D. (2005). Elicited priors for bayesian model specifications in political science research. The Journal of Politics, 67(3):841–872.
dc.relationGuttman, I. (1967). The use of the concept of a future observation in goodness-of-fit problems. Journal of the Royal Statistical Society: Series B (Methodological), 29(1):83–100.
dc.relationHagemann, S. (2007). Applying ideal point estimation methods to the council of ministers. European Union Politics, 8(2):279–296.
dc.relationHahn, E. D. and Soyer, R. (2005). Probit and logit models: Differences in the multivariate realm. The Journal of the Royal Statistical Society, Series B, pages 1–12.
dc.relationHahn, R. P., Carvalho, C. M., and Scott, J. G. (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.
dc.relationHare, C., Armstrong, D. A., Bakker, R., Carroll, R., and Poole, K. T. (2015). Using bayesian Aldrich Mckelvey scaling to study citizens’ ideological preferences and perceptions. American Journal of Political Science, 59(3):759–774.
dc.relationHinich, M. J. and Munger, M. C. (1996). Ideology and the theory of political choice. University of Michigan Press.
dc.relationHinich, M. J., Munger, M. C., et al. (1997). Analytical politics. Cambridge university press.
dc.relationHinich, M. J. and Ordeshook, P. C. (1970). Plurality maximization vs vote maximization: A spatial analysis with variable participation. The American Political Science Review, 64(3):772–791.
dc.relationHiroi, T. and Renn´o, L. (2016). Agenda setting and gridlock in a multi-party coalitional presidential system, the case of Brazil. Legislative institutions and lawmaking in Latin America, pages 61–91.
dc.relationHix, S., Noury, A., and Roland, G. (2005). Power to the parties: cohesion and competition in the european parliament 1979-2001. British Journal of Political Science, pages 209–234.
dc.relationHoff, P. D. (2009). A first course in Bayesian statistical methods, volume 580. Springer.
dc.relationHoskin, G. (1975). Dimensions of conflict in the colombian national legislature. Legislative Systems in Developing Countries, pages 143–178.
dc.relationHoskin, G. (1979). Belief systems of Colombian political party activists. Journal of Interamerican Studies and World Affairs, 21(4):481–504.
dc.relationHoskin, G., Kline, H. F., and Buitrago, F. L. (1976). Legislative Behavior in Colombia. Council on International Studies, State University of New York at Buffalo.
dc.relationHoskin, G. and Swanson, G. (1973). Inter-party competition in Colombia: a return to la violencia? American Journal of Political Science, pages 316–350.
dc.relationHoskin, G. and Swanson, G. (1974). Political party leadership in Colombia: [a] spatial analysis. Compa rative politics, 6(3):395–423
dc.relationHowell, W., Adler, S., Cameron, C., and Riemann, C. (2000). Divided government and the legislative productivity of congress, 1945-94. Legislative Studies Quarterly, pages 285–312
dc.relationJackman, S. (2001). Multidimensional analysis of roll call data via bayesian simulation: Identification, estimation, inference, and model checking. Political Analysis, 9(3):227–241.
dc.relationJackman, S. (2004). Bayesian analysis for political research. Annu. Rev. Polit. Sci., 7:483–505.
dc.relationJackman, S. (2009). Bayesian analysis for the social sciences, volume 846. John Wiley & Sons.
dc.relationJackman, S., Tahk, A., Zeileis, A., Maimone, C., Fearon, J., and Meers, Z. (2020). The pscl package. Software. https://cran.r-project.org/web/packages/pscl/pscl.pdf.
dc.relationJohnson, V. E. et al. (2007). Bayesian model assessment using pivotal quantities. Bayesian Analysis, 2(4):719–733.
dc.relationJones, M. P. and Hwang, W. (2005a). Party government in presidential democracies: Extending cartel theory beyond the US congress. American Journal of Political Science, 49(2):267–282.
dc.relationJones, M. P. and Hwang, W. (2005b). Provincial party bosses: Keystone of the Argentine Congress, pages 115–138. Pennsylvania State University Press University Park.
dc.relationKass, R. E. and Wasserman, L. (1996). The selection of prior distributions by formal rules. Journal of the American statistical Association, 91(435):1343–1370.
dc.relationKline, H. F. (1974). Interest groups in the colombian congress: Group behavior in a centralized, patri monial political system. Journal of Interamerican Studies and World Affairs, 16(3
dc.relationKline, H. F. (1977). Committee membership turnover in the colombian national congress, 1958-1974. Legislative Studies Quarterly, pages 29–43.
dc.relationKrehbiel, K. (1988). Spatial models of legislative choice. Legislative Studies Quarterly, pages 259–319.
dc.relationKrehbiel, K. (1998). Pivotal politics: A theory of US lawmaking. University of Chicago Press.
dc.relationKromer, M. K. (2005). Determinants of abstention in the United States house of representatives: an analysis of the 102nd through the 107th sessions. Master’s thesis, Louisiana State University, Baton Rouge, LA.
dc.relationKruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
dc.relationLee, J. M. (2018). Introduction to Riemannian manifolds. Springer.
dc.relationLewis, J. B. and Poole, K. T. (2004). Measuring bias and uncertainty in ideal point estimates via the parametric bootstrap. Political Analysis, pages 105–127.
dc.relationLofland, C. L., Rodr´ıguez, A., Moser, S., et al. (2017). Assessing differences in legislators’ revealed preferences: A case study on the 107th US senate. The Annals of Applied Statistics, 11(1):456–479.
dc.relationLunn, D., Jackson, C., Best, N., Thomas, A., and Spiegelhalter, D. (2013). The BUGS book. A Practical Introduction to Bayesian Analysis, Chapman Hall, London.
dc.relationMacRae, D. (1952). The relation between roll call votes and constituencies in the Massachusetts house of representatives. American Political Science Review, 46(4):1046–1055.
dc.relationMacRae, D. (1958). Dimensions of congressional voting: A statistical study of the house of representatives in the Eighty-first congress. The Journal of Politics, 1(3).
dc.relationMacRae, D. (1965). A method for identifying issues and factions from legislative votes. The American Political science Review, 59(4):909–926.
dc.relationManski, C. F. (1977). The structure of random utility models. Theory and decision, 8(3):229.
dc.relationMartin, A. D. and Quinn, K. M. (2002). Dynamic ideal point estimation via Markov Chain Monte Carlo for the US supreme court, 1953–1999. Political analysis, 10(2):134–153.
dc.relationMartin, A. D., Quinn, K. M., Park, J. H., and Park, M. J. H. (2020). Package MCMCpack.
dc.relationMayhew, D. R. (1974). Congress: The electoral connection. Yale university press.
dc.relationMcCarty, N., Poole, K. T., and Rosenthal, H. (2001). The hunt for party discipline in congress. American Political Science Review, pages 673–687.
dc.relationMcCullagh, P. (2018). Generalized linear models. Routledge.
dc.relationMcDonnell, R. M. (2017). Formal comparisons of legislative institutions: Ideal points from brazilian legislatures. Brazilian Political Science Review, 11(1).
dc.relationMcDonnell, R. M., Duarte, G. J., and Freire, D. (2019). congressbr: An R package for analyzing data from Brazil’s chamber of deputies and federal senate. Latin American Research Review, 54(4).
dc.relationMcFadden, D. L. (1976). Quantal choice analaysis: A survey. In Annals of Economic and Social Measu rement, Volume 5, number 4, pages 363–390. NBER.
dc.relationMcKelvey, R. D., Ordeshook, P. C., and Winer, M. D. (1978). The competitive solution for n-person games without transferable utility, with an application to committee games. American Political Science Review, 72(2):599–615.
dc.relationMeng, X.-L. et al. (1994). Posterior predictive p-values. The Annals of Statistics, 22(3):1142–1160.
dc.relationMeyn, S. P. and Tweedie, R. L. (2012). Markov chains and stochastic stability. Springer Science & Business Media.
dc.relationMiller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological review, 63(2):81.
dc.relationMoser, S., Rodr´ıguez, A., and Lofland, C. L. (2021). Multiple ideal points: Revealed preferences in different domains. Political Analysis, 29(2):139–166.
dc.relationMurray, J. S., Dunson, D. B., Carin, L., and Lucas, J. E. (2013). Bayesian gaussian copula factor models for mixed data. Journal of the American Statistical Association, 108(502):656–665.
dc.relationNeto, O. A. (2002). Presidential cabinets, electoral cycles, and coalition discipline in Brazil. Legislative Politics in Latin America, pages 48–78.
dc.relationNeto, O. A. (2006). The presidential calculus: Executive policy making and cabinet formation in the americas. Comparative Political Studies, 39(4):415–440.
dc.relationNorris, J. R. (1998). Markov chains. Cambridge university press.
dc.relationOsorio, C. (2014). Unidad nacional: crítica, pero muy útil. Congreso Visible.
dc.relationPachón, M. (2011). ¿qué tanta política nacional discute un congreso? una comparación de las agendas de las plenarias y comisiones posterior a la constitución de 1991. Revista latinoamericana de política comparada, 4:75–98.
dc.relationPachón, M. and Johnson, G. B. (2016). When’s the party (or coalition)? agenda-setting in a highly fragmented, decentralized legislature. Journal of Politics in Latin America, 8(2):71–100.
dc.relationPachón, M. and Muñoz, M. (2020). Policy analysis and the legislature in Colombia, pages 81–98. Bristol University Press, first edition.
dc.relationPati, D., Bhattacharya, A., Pillai, N. S., Dunson, D., et al. (2014). Posterior contraction in sparse Bayesian factor models for massive covariance matrices. Annals of Statistics, 42(3):1102–1130.
dc.relationPatz, R. J. and Junker, B. W. (1999). A straightforward approach to Markov chain Monte Carlo methods for item response models. Journal of educational and behavioral Statistics, 24(2):146–178.
dc.relationPereira, C. and Mueller, B. (2004a). The cost of governing: Strategic behavior of the president and legislators in Brazil’s budgetary process. Comparative Political Studies, 37(7):781–815.
dc.relationPereira, C. and Mueller, B. (2004b). A theory of executive dominance of congressional politics: the committee system in the brazilian chamber of deputies. The Journal of Legislative Studies, 10(1):9–49.
dc.relationPolson, N. G., Scott, J. G., and Windle, J. (2013). Bayesian inference for logistic models using p´olya– gamma latent variables. Journal of the American statistical Association, 108(504):1339–1349.
dc.relationPonce, A. (2016). Strong presidents, weak parties, and agenda setting. Lawmaking in democratic Peru. Legislative institutions and lawmaking in Latin America, pages 175–198.
dc.relationPoole, K., Lewis, J., and Lo, M. J. (2018). Package ‘wnominate’.
dc.relationPoole, K. T. (2000). Nonparametric unfolding of binary choice data. Political Analysis, 8(3):211–237.
dc.relationPoole, K. T. (2005). Spatial models of parliamentary voting. Cambridge University Press.
dc.relationPoole, K. T. (2007). Changing minds? Not in congress! Public Choice, 131(3-4):435–451.
dc.relationPoole, K. T. and Rosenthal, H. (1984). US presidential elections 1968-80: A spatial analysis. American Journal of Political Science, pages 282–312.
dc.relationPoole, K. T. and Rosenthal, H. (1985). A spatial model for legislative roll call analysis. American Journal of Political Science, pages 357–384.
dc.relationPoole, K. T. and Rosenthal, H. (1987). Analysis of congressional coalition patterns: A unidimensional spatial model. Legislative Studies Quarterly, pages 55–75.
dc.relationPotoski, M. and Talbert, J. (2000). The dimensional structure of policy outputs: Distributive policy and roll call voting. Political Research Quarterly, 53(4):695–710.
dc.relationQuinn, K. M. (2004). Bayesian factor analysis for mixed ordinal and continuous responses. Political Analysis, 12(4):338–353.
dc.relationRaftery, A. E. and Lewis, S. (1991). How many iterations in the gibbs sampler? Technical report, Washington University Seattle Departament of Statistics.
dc.relationRivers, D. (2003). Identification of multidimensional item-response models. Typescript. Department of Political Science, Stanford University.
dc.relationRobert, C. and Casella, G. (2013). Monte Carlo statistical methods. Springer Science & Business Media.
dc.relationRoberts, J. M., Smith, S. S., and Haptonstahl, S. R. (2016). The dimensionality of congressional voting reconsidered. American Politics Research, 44(5):794–815
dc.relationRodriguez, A. (2012). Modeling the dynamics of social networks using bayesian hierarchical blockmodels. Statistical Analysis and Data Mining: The ASA Data Science Journal, 5(3):218–234.
dc.relationRodríguez, A. and Moser, S. (2015). Measuring and accounting for strategic abstentions in the US senate, 1989–2012. Journal of the Royal Statistical Society: Series C: Applied Statistics, pages 779–797.
dc.relationRodríguez, J. A. (2017). Decentralization (and centralization) without representation: On the territorial composition of the Colombian Congress. Centro Editorial Facultad de Ciencias Econ´omicas. Universidad Nacional.
dc.relationRomer, T. and Rosenthal, H. (1978). Political resource allocation, controlled agendas, and the status quo. Public choice, 33(4):27–43.
dc.relationRosas, G. (2005). The ideological organization of latin american legislative parties: An empirical analysis of elite policy preferences. Comparative Political Studies, 38(7):824–849.
dc.relationRosas, G. and Shomer, Y. (2008). Models of nonresponse in legislative politics. Legislative Studies Quarterly, 33(4):573–601.
dc.relationRosas, G., Shomer, Y., and Haptonstahl, S. R. (2015). No news is news: Nonignorable nonresponse in roll-call data analysis. American Journal of Political Science, 59(2):511–528.
dc.relationRubin, D. B. (1984). Bayesianly justifiable and relevant frequency calculations for the applies statistician. The Annals of Statistics, pages 1151–1172.
dc.relationRubinstein, R. Y. and Kroese, D. P. (2016). Simulation and the Monte Carlo method, volume 10. John Wiley & Sons.
dc.relationRue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., and Lindgren, F. K. (2017). Bayesian computing with INLA: a review. Annual Review of Statistics and Its Application, 4:395–421.
dc.relationSavage, L. J. (1972). The foundations of statistics. Courier Corporation.
dc.relationSchickler, E. (2000). Institutional change in the house of representatives, 1867-1998: a test of partisan and ideological power balance models. American Political Science Review, pages 269–288.
dc.relationScott, J. G. and Berger, J. O. (2006). An exploration of aspects of Bayesian multiple testing. Journal of statistical planning and inference, 136(7):2144–2162.
dc.relationScott, J. G. and Berger, J. O. (2010). Bayes and empirical-bayes multiplicity adjustment in the variable selection problem. The Annals of Statistics, pages 2587– 2619
dc.relationSewell, D. K. and Chen, Y. (2015). Latent space models for dynamic networks. Journal of the American Statistical Association, 110(512):1646–1657.
dc.relationShepsle, K. A. (1979). Institutional arrangements and equilibrium in multidimensional voting models. American Journal of Political Science, pages 27–59.
dc.relationShepsle, K. A. and Weingast, B. R. (1987). The institutional foundations of committee power. The American Political Science Review, pages 85–104.
dc.relationSherina, V., McCall, M. N., and Love, T. M. (2019). Fully Bayesian imputation model for non-random missing data in qPCR. arXiv preprint arXiv:1910.13936.
dc.relationShor, B., Berry, C., and McCarty, N. (2010). A bridge to somewhere: Mapping state and congressional ideology on a cross-institutional common space. Legislative Studies Quarterly, 35(3):417–448.
dc.relationShor, B. and McCarty, N. (2011). The ideological mapping of American legislatures. American Political Science Review, pages 530–551.
dc.relationSnyder Jr, J. M. and Groseclose, T. (2000). Estimating party influence in congressional roll-call voting. American Journal of Political Science, pages 193–211.
dc.relationSpiegelhalter, D. J., Best, N. G., Carlin, B. P., and Van Der Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the royal statistical society: Series b (statistical methodology), 64(4):583–639.
dc.relationSpiegelhalter, D. J., Best, N. G., Carlin, B. P., and Van der Linde, A. (2014). The deviance information criterion: 12 years on. Journal of the Royal Statistical Society: Series B: Statistical Methodology, pages 485–493.
dc.relationSteinbakk, G. H. and Storvik, G. O. (2009). Posterior predictive p-values in Bayesian hierarchical models. Scandinavian Journal of Statistics, 36(2):320–336.
dc.relationStuart, A., Arnold, S., Ord, J. K., O’Hagan, A., and Forster, J. (1994). Kendall’s advanced theory of statistics. Wiley
dc.relationTajfel, H. (1981). Human groups and social categories: Studies in social psychology. Cup Archive.
dc.relationTalbert, J. C. and Potoski, M. (2002). Setting the legislative agenda: The dimensional structure of bill cosponsoring and floor voting. Journal of Politics, 64(3):864–891.
dc.relationThurner, P. W. (2000). The empirical application of the spatial theory of voting in multiparty systems with random utility models. Electoral Studies, 19(4):493–517.
dc.relationTierney, L. (1994). Markov chains for exploring posterior distributions. the Annals of Statistics, pages 1701–1728.
dc.relationTreier, S. and Jackman, S. (2008). Democracy as a latent variable. American Journal of Political Science, 52(1):201–217.
dc.relationTsai, T.-h. (2020). The influence of the president and government coalition on roll-call voting in Brazil, 2003–2006. Political Studies Review, page 1-16.
dc.relationVoeten, E. (2000). Clashes in the assembly. International organization, pages 185–215.
dc.relationVoeten, E. (2013). Data and analyses of voting in the United Nations General Assembly. In Routledge handbook of international organization, pages 80–92. Routledge.
dc.relationWainer, P. W. H. H. (1993). Differential item functioning. Psychology Press.
dc.relationWatanabe, S. (2013). WAIC and WBIC are information criteria for singular statistical model evaluation. In Proceedings of the Workshop on Information Theoretic Methods in Science and Engineering, pages 90–94.
dc.relationWeisberg, H. F. and Rusk, J. G. (1970). Dimensions of candidate evaluation. The American Political Science Review, 64(4):1167–1185.
dc.relationWest, M. and Harrison, J. (2006). Bayesian forecasting and dynamic models. Springer Science & Business Media.
dc.relationWestern, B. and Jackman, S. (1994). Bayesian inference for comparative research. American Political Science Review, pages 412–423.
dc.relationWills-Otero, L. (2014). Reformas constitucionales y leyes sancionadas. Congreso Visible.
dc.relationWolters, M. (1978). Models of roll-call behavior. Political Methodology, pages 7–54.
dc.relationYu, X. (2020). Spherical Latent Factor Model for Binary and Ordinal Data. PhD thesis, UC Santa Cruz.
dc.relationYu, X. and Rodriguez, A. (2019a). Spatial voting models in circular spaces: A case study of the U.S. house of representatives. Available at SSRN 3381925.
dc.relationYu, X. and Rodriguez, A. (2019b). Spherical latent factor model. Available at SSRN 3381925
dc.relationZellner, A. (1986). On assessing prior distributions and bayesian regression analysis with g-prior distri butions. Bayesian inference and decision techniques
dc.relationZucco, C. (2009). Ideology or what? Legislative behavior in multiparty presidential settings. The Journal of Politics, 71(3):1076–1092.
dc.relationZucco, C. (2013). Legislative coalitions in presidential systems: the case of Uruguay. Latin American politics and society, 55(1):96–118.
dc.relationZucco, C. and Lauderdale, B. E. (2011). Distinguishing between influences on brazilian legislative beha vior. Legislative Studies Quarterly, 36(3):363–396.
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.rightsAbierto (Texto Completo)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.titleMétodos Bayesianos para caracterizar el comportamiento legislativo del Senado colombiano en el periodo 2010 - 2014


Este ítem pertenece a la siguiente institución