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Below results based on the criteria 'MCMC.'
Total number of records returned: 13

1
Paper
Bayesian and Frequentist Inference for Ecological Inference: The RxC Case
Rosen, Ori
King, Gary
Jiang, Wenxin
Tanner, Martin A.

Uploaded 07-25-2000
Keywords EM
MCMC
least squares
ecological inference
Bayes
Nazi
Abstract In this paper we propose Bayesian and frequentist approaches to ecological inference, based on RxC contingency tables, including a covariate. The proposed Bayesian model extends the binomial-beta hierarchical model developed by King, Rosen and Tanner (1999) from the 2x2 case to the RxC case. As in the 2x2 case, the inferential procedure employs Markov chain Monte Carlo (MCMC) methods. As such, the resulting MCMC analysis is rich but computationally intensive. The frequentist approach, based on first moments rather than on the entire likelihood, provides quick inference via nonlinear least-squares, while retaining good frequentist properties. The two approaches are illustrated with simulated data, as well as with real data on voting patterns in Weimar Germany. In the final section of the paper we provide an overview of a range of alternative inferential approaches which trade-off computational intensity for statistical efficiency.

2
Paper
Estimation Of Electoral Disproportionality And Thresholds Via MCMC
Kalandrakis, Anastassios

Uploaded 11-03-1999
Keywords Electoral Disproportionality
Electoral Thresholds
Gibbs Sampling
MCMC
Metropolis Algorithm
Abstract For statistical as well as political reasons -- some already identified in the literature -- measures of both electoral disproportionality and electoral thresholds are essential and must be combined in numerical summaries of electoral institutions. With few exceptions, none of these quantities can be reliably inferred directly from the provisions of the electoral law, thus impairing "large scale" comparative studies. Through the use of sampling based Bayes methods I am able to simultaneously estimate these two quantities from electoral returns. I apply the proposed procedure on 45 electoral systems in use over 216 elections to the national parliaments in the 15 countries of the European Union in the period 1945-1996. The resultant two-dimensional summary of electoral systems has several attractive properties in comparison to indices of disproportionality currently used in comparative politics.

3
Paper
Operationalizing and Testing Spatial Theories of Voting
Quinn, Kevin M.
Martin, Andrew D.

Uploaded 04-15-1998
Keywords spatial voting
factor analysis
multinomial probit
multinomial logit
Bayesian inference
model comparison
Bayes factors
MCMC
Dutch politics
Danish politics
Abstract Spatial models of voting behavior provide the foundation for a substantial number of theoretical results. Nonetheless, empirical work involving the spatial model faces a number of potential difficulties. First, measures of the latent voter and candidate issue positions must be obtained. Second, evaluating the fit of competing statistical models of voter choice is often more complicated than previously realized. In this paper, we discuss precisely these issues. We argue that confirmatory factor analysis applied to mass-level issue preference questions is an attractive means of measuring voter ideal points. We also show how party issue positions can be recovered using a variation of this strategy. We go on to discuss the problems of assessing the fit of competing statistical models (multinomial logit vs. multinomial probit) and competing explanations (those based on spatial theory vs. those derived from other theories of voting such as sociological theories). We demonstrate how the Bayesian perspective not only provides computational advantages in the case of fitting the multinomial probit model, but also how it facilitates both types of comparison mentioned above. Results from the Netherlands and Denmark suggest that even when the computational cost of multinomial probit is disregarded, the decision whether to use multinomial probit (MNP) or multinomial logit (MNL) is not clear-cut.

4
Paper
Binomial-Beta Hierarchical Models for Ecological Inference
King, Gary
Rosen, Ori
Tanner, Martin A.

Uploaded 05-28-1998
Keywords ecological inference
aggregation
MCMC
hierarchical models
iterative simulation
Abstract We develop a binomial-beta hierarchical model for ecological inference, using insights from King's (1997) ecological inference model and from the literature on hierarchical models based on Markov chain Monte Carlo algorithms (Tanner, 1996). Models in the framework we provide appear to scale up well, to have few numerical difficulties, and to recognize and avoid automatically problems with multiple modes and some other statistical issues.

5
Paper
Listwise Deletion is Evil: What to Do About Missing Data in Political Science
King, Gary
Honaker, James
Joseph, Anne
Scheve, Kenneth

Uploaded 07-13-1998
Keywords missing data
imputation
IP
EM
EMs
EMis
data augmentation
MCMC
importance sampling
item nonresponse
Abstract We address a substantial discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing data problems based on the concept of ``multiple imputation,'' but most researchers in our field and other social sciences still use far inferior methods. Indeed, we demonstrate that the threats to validity from current missing data practices rival the biases from the much better known omitted variable problem. This discrepancy is not entirely our fault, as the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise on the part of the user (indeed, even experts disagree on how to use them). In this paper, we adapt an existing algorithm, and use it to implement a general-purpose, multiple imputation model for missing data. This algorithm is between 20 and 100 times faster than the leading method recommended in the statistics literature and is very easy to use. We also quantify the considerable risks of current political science missing data practices, illustrate how to use the new procedure, and demonstrate the advantages of our approach to multiple imputation through simulated data as well as via replications of existing research.

6
Paper
Listwise Deletion is Evil: What to Do About Missing Data in Political Science (revised)
King, Gary
Honaker, James
Joseph, Anne
Scheve, Kenneth

Uploaded 08-19-1998
Keywords missing data
imputation
IP
EM
EMs
EMis
data augmentation
MCMC
importance sampling
item nonresponse
Abstract We propose a remedy to the substantial discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. With a few notable exceptions, statisticians and methodologists have agreed on a widely applicable approach to many missing data problems based on the concept of ``multiple imputation,'' but most researchers in our field and other social sciences still use far inferior methods. Indeed, we demonstrate that the threats to validity from current missing data practices rival the biases from the much better known omitted variable problem. As it turns out, this discrepancy is not entirely our fault, as the computational algorithms used to apply the best multiple imputation models have been slow, difficult to implement, impossible to run with existing commercial statistical packages, and demanding of considerable expertise on the part of the user (even experts disagree on how to use them). In this paper, we adapt an existing algorithm, and use it to implement a general-purpose, multiple imputation model for missing data. This algorithm is between 65 and 726 times faster than the leading method recommended in the statistics literature and is very easy to use. We also quantify the considerable risks of current political science missing data practices, illustrate how to use the new procedure, and demonstrate the advantages of our approach to multiple imputation through simulated data as well as via replications of existing research. We also offer easy-to-use public domain software that implements our approach.

7
Paper
Senate Voting on NAFTA: The Power and Limitations of MCMC Methods for Studying Voting across Bills and across States
Smith, Alastair
McGillivray, Fiona

Uploaded 07-09-1996
Keywords NAFTA
MCMC
Gibbs sampling
bivariate probit
Senate
Abstract We examine similarities in senate voting within states and across two senate bills: the 1991 fast track authorization bill and the 1993 NAFTA implementation bill. A series of bivariate probit models are estimated by Markov Chain Monte Carlo simulation. We discuss the power of MCMC techniques and how the output of these sampling procedures can be used for Bayesian model comparisons. Having separately explored the similarities in votes across bills and within states, we develop a 4-variate probit model to explain voting on NAFTA. The power of MCMC techniques to estimate this complicated model is demonstrated with two different MCMC procedures. We conclude by discussing the data requirements for these techniques.

8
Paper
Analyzing the US Senate in 2003: Similarities, Networks, Clusters and Blocs
Jakulin, Aleks

Uploaded 10-27-2004
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 information-theoretic 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, multi-dimensional 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 bloc-voting 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 what-if analysis.

9
Paper
A Compositional-Hierarchical Model of Abstention under Compulsory Voting (poster)
Katz, Gabriel

Uploaded 06-18-2008
Keywords compulsory voting
abstention
compositional data
hierarchical modelling
MCMC.
Abstract Invalid voting and electoral absenteeism are two important sources of abstention in compulsory voting systems. Previous studies in this area have not considered the correlation between both variables and ignored the compositional nature of the data, potentially leading to unfeasible results and discarding helpful information from an inferential standpoint. In order to overcome these problems, this paper develops a statistical model that accounts for the compositional and hierarchical structure of the data and addresses robustness concerns raised by the use of small samples that are typical in the literature. The model is applied to analyze invalid voting and electoral absenteeism in Brazilian legislative elections between 1945 and 2006 via MCMC simulations. The results show considerable differences in the determinants of both forms of non-voting; while invalid voting was strongly positively related both to political protest and to the existence of important informational barriers to voting, the influence of these variables on absenteeism is less evident. Comparisons based on posterior simulations indicate that the model developed in this paper fits the dataset better than several alternative modeling approaches and leads to different substantive conclusions regarding the effect of different predictors on the both sources of abstention.

10
Paper
Binary and Ordinal Time Series with AR(p) Errors: Bayesian Model Determination for Latent High-Order Markovian Processes
Pang, Xun

Uploaded 07-06-2008
Keywords Autoregressive Errors
Auxiliary Particle Filter
Fixed-lag Smoothing
Markov Chain Monte Carlo (MCMC)
Political Science
Sampling Importance Resampling(SIR)
Abstract To directly and adequately correct serial correlation in binary and ordinal response data, this paper proposes a probit model with errors following a pth-order autoregressive process, and develops simulation-based methods in the Bayesian context to handle computational challenges of posterior estimation, model comparison, and lag order determination. Compared to the extant methods, such as quasi-ML, GCM, and and simulation-based ML estimators, the current method does not rely on the properties of the big variance-covariance matrix or the shape of the likelihood function. In addition, the present model efficiently handles high-order autocorrelated errors that raise computationally formidable difficulties to the conventional methods. By applying a mixed sampler of the Gibbs and Metropolis-Hastings algorithm, the posterior distributions of the parameters do not depend on initial observations. The auxiliary particle filter, complemented by the fixed-lag smoothing, is extended to approximate Bayes Factors for models with latent high-order Markov processes. Computational methods are tested with empirical data. Energy cooperation policies of the International Energy Agency are analyzed in terms of their effects on global oil-supply security. The current model with different lag orders, together with other competitive models, is estimated and compared.

11
Paper
Detecting heterogeneous treatment effects in large-scale experiments using Bayesian Additive Regression Trees
Green, Donald
Kern, Holger

Uploaded 07-16-2010
Keywords causal inference
heterogeneity
ATE
ensemble methods
BART
tree models
MCMC
Abstract We present a method that largely automates the search for systematic treatment effect heterogeneity in large-scale experiments. We introduce an estimator recently proposed in the statistical learning literature, Bayesian Additive Regression Trees (BART), to model treatment effects that vary as a function of covariates. BART has two important advantages over commonly employed parametric modeling strategies: it automates the search for treatment-covariate interactions and models them in a very flexible manner. To increase the reliability and credibility of the resulting conditional average treatment effect estimates, we suggest the use of a split sample analysis, which randomly divides the data into two equally-sized parts. The first part is used to search for systematic treatment effect heterogeneity; the second part is used to confirm the results. This approach permits a relatively unstructured exploration of systematic treatment effect heterogeneity while avoiding the pitfalls of data dredging and multiple comparisons. We illustrate the value of our approach by offering two empirical examples, a survey experiment on Americans' support for social welfare spending and a voter mobilization field experiment. In both applications, our approach provides robust insights into the nature and extent of systematic treatment effect heterogeneity.

12
Paper
Bayesian Methods: A Social and Behavioral Sciences Approach, ANSWER KEY TO THE SECOND EDITION. Odd Numbers.
Park, Hong Min
Gill, Jeff

Uploaded 09-14-2010
Keywords Bayes
modeling
simulation
Bayesian inference
MCMC
prior
posterior
Bayes Factor
DIC
GLM
Markov chain
Monte Carlo
hierarchical models
linear
nonlinear
Abstract This is the odd-numbered exercise answers to the second edition of Bayesian Methods: A Social and Behavioral Sciences Approach (minus Chapter 13). Course Instructors can get the full set from Chapman & Hall/CRC upon request.

13
Paper
Alternative Models of Dynamics in Binary Time-Series--Cross-Section Models: The Example of State Failure
Beck, Nathaniel
Jackman, Simon
Epstein, David
O'Halloran, Sharyn

Uploaded 07-14-2001
Keywords dynamic probit
btscs
state failure
Gibbs sampling
MCMC
transitional models
discrete data
ROC
correlated binary data
generalized residuals
Abstract This paper investigates a variety of dynamic probit models for time-series--cross-section data in the context of explaining state failure. It shows that ordinary probit, which ignores dynamics, is misleading. Alternatives that seem to produce sensible results are the transition model and a model which includes a lagged emph{latent} dependent variable. It is argued that the use of a lagged latent variable is often superior to the use of a lagged realized dependent variable. It is also shown that the latter is a special case of the transition model. The relationship between the transition model and event history methods is also considered: the transition model estimates an event history model for both values of the dependent variable, yielding estimates that are identical to those produced by the two event history models. Furthermore, one can incorporate the insights gleaned from the event history models into the transition analysis, so that researchers do not have to assume duration independence. The conclusion notes that investigations of the various models have been limited to data sets which contain long sequences of zeros; models may perform differently in data sets with shorter bursts of zeros and ones.


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