
1 
Paper

Cosponsorship Coalitions in the U.S. House of Representatives
Grant, J. Tobin
Pellegrini, Pasquale (Pat) A.

Uploaded 
04221998

Keywords 
clustering coalitions cosponsorship duration models hazard models heterogeneity spatial models

Abstract 
urrent theories and methods for studying of cosponsorship assume that
the decision to cosponsor is identical to decision to vote. In this
paper we develop a new theory of cosponsorship that identifies where
along the ideological spectrum cosponsors of a bill are more likely to
be. Moreover, we predict that members with organizational ties to the
sponsor are more likely to cosponsor than other members. To test this
theory, we employ a spatial duration model. This method has recently
been used by geographers to estimate areas that are more likely to
experience an "event." Using this technique permits a statistical test
that supports our substantive hypotheses that cosponsorship coalitions
are shaped by the characteristics of the location of the bill, the
shared ties to the sponsor, and the policy area. In addition, more
active sponsors are associated with wider and less clustered coalitions.
These findings demonstrate that theories of the voting decision are not
applicable to cosponsorship. 

2 
Paper

Analyzing the US Senate in 2003: Similarities, Networks, Clusters and Blocs
Jakulin, Aleks

Uploaded 
10272004

Keywords 
roll call analysis latent variable models MCMC information theory clustering visualization

Abstract 
To analyze the roll calls in the US Senate in year 2003, we have employed the methods already used throughout the science community for analysis of genes, surveys and text. With informationtheoretic measures we assess the association between pairs of senators based on the votes they cast. Furthermore, we can evaluate the influence of a voter by postulating a Shannon information channel between the outcome and a voter. The matrix of associations can be summarized using hierarchical clustering, multidimensional scaling and link analysis. With a discrete latent variable model we identify blocs of cohesive voters within the Senate, and contrast it with continuous ideal point methods. Under the blocvoting model, the Senate can be interpreted as a weighted vote system, and we were able to estimate the empirical voting power of individual blocs through whatif analysis. 

3 
Paper

Attributing Effects to A Cluster Randomized GetOutTheVote Campaign: An Application of Randomization Inference Using Full Matching
Bowers, Jake
Hansen, Ben

Uploaded 
07182005

Keywords 
causal inference randomization inference attributable effects full matching instrumental variables missing data field experiments clustering

Abstract 
Statistical analysis requires a probability model: commonly, a model
for the dependence of outcomes $Y$ on confounders $X$ and a
potentially causal variable $Z$. When the goal of the analysis is to
infer $Z$'s effects on $Y$, this requirement introduces an element
of circularity: in order to decide how $Z$ affects $Y$, the analyst
first determines, speculatively, the manner of $Y$'s dependence on
$Z$ and other variables. This paper takes a statistical perspective
that avoids such circles, permitting analysis of $Z$'s effects on
$Y$ even as the statistician remains entirely agnostic about the
conditional distribution of $Y$ given $X$ and $Z$, or perhaps even
denies that such a distribution exists. Our assumptions instead
pertain to the conditional distribution $Z vert X$, and the role of
speculation in settling them is reduced by the existence of random
assignment of $Z$ in a field experiment as well as by
poststratification, testing for overt bias before accepting a
poststratification, and optimal full matching. Such beginnings pave
the way for ``randomization inference'', an approach which, despite
a long history in the analysis of designed experiments, is
relatively new to political science and to other fields in which
experimental data are rarely available.
The approach applies to both experiments and observational studies.
We illustrate this by applying it to analyze A. Gerber and
D. Green's New Haven Vote 98 campaign. Conceived as both a
getoutthevote campaign and a field experiment in political
participation, the study assigned households to treatment and
desired to estimate the effect of treatment on the individuals
nested within the households. We estimate the number of voters who
would not have voted had the campaign not prompted them to  that
is, the total number of votes attributable to the interventions of
the campaigners  while taking into account the nonindependence
of observations within households, nonrandom compliance, and
missing responses. Both our statistical inferences about these
attributable effects and the stratification and matching that
precede them rely on quite recent developments from statistics; our
matching, in particular, has novel features of potentially wide
applicability. Our broad findings resemble those of the original
analysis by citet{gerbergreen00}. 

4 
Poster

Predicting oil nationalization: Comparing estimates from a Bayesian Hierarchical Model to a Mixture Model
Mahdavi, Paasha

Uploaded 
07172013

Keywords 
Bayesian mixture models Bayesian longitudinal models Modelbased clustering Prediction Crossvalidation Resource curse

Abstract 
Recent expropriations in the oil sectors of Argentina, Bolivia, and Venezuela have renewed interest in the study of nationalizations in the oil industry. Employing Bayesian estimation methods, this study seeks to answer two substantive questions and two methodological questions. First, what is the probability of oil nationalization in a given country, in a given year? Second, what international and countrylevel factors influence this probability? Third, are there differences in predictive results when using the Bayesian hierarchical approach vs. the Bayesian mixture modeling approach? Fourth, does the assumption that countries can be clustered into groups change the model estimates? The results indicate a 1.1\% probability of nationalization in a given country in a given year with the strongest empirical predictors of nationalization being global oil prices, a country's oil production history, and the diffusion of previous nationalizations. Methodologically, the findings here suggest little if any difference between using the hierarchical model framework compared to the mixture model framework. Overall, adding the assumption of clustering by country only slightly improves predictive accuracy while maintaining similar fixed effects model estimates. 

5 
Poster

Blossom: An evolutionary strategy optimizer with applications to matching, scaling, networks, and sampling
Beauchamp, Nick

Uploaded 
07242013

Keywords 
maximum likelihood genetic algorithms matching multidimensional scaling social networks clustering sampling methods markov chain monte carlo evolutionary algorithms estimation of distribution algorithms

Abstract 
This paper introduces a new maximization and importance sampling algorithm, "Blossom," along with an associated R script, which is especially well suited to rugged, discontinuous, and multimodal functions where even approximate gradient methods are unfeasible, and MCMC approaches work poorly. The Blossom algorithm employs an evolutionary optimization strategy related to the Estimation of Multivariate Normal Algorithm (EMNA) or Covariance Matrix Adaptation (CMA), within the general family of Estimation of Distribution Algorithms (EDA). It works by successive iterations of sampling, selecting the highestscoring subsample, and using the variancecovariance matrix of that subsample to generate a new sample, with various selfadapting parameters. Compared against a benchmark suite of challenging functions introduced in Yao, Liu, and Lin (1999), it finds equal or better maxima to those found by the genetic algorithm Genoud introduced in Mebane and Sekhon (2011). The algorithm is then tested in four challenging domains from political science: (1) estimation of nonlinear and multimodal spatial metrics; (2) maximizing balance for matching; (3) ideological scaling of judges with discontinuous objective functions; (4) community detection in social networks. In all of these cases, Blossom outperforms most existing nonlinear optimizers in R. Finally, the samples gathered during the optimization process can be efficiently used for importance sampling using approximate voronoi cells around sample points, equalling the performance of MCMC metropolis samplers in some circumstances, and also of use for generating efficient proposal distributions. Even in an increasingly MCMC world, there remain important roles for effective generalpurpose optimizers, and Blossom is especially effective for rough terrains where most other methods fail. 

