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18
Measuring DistrictLevel Partisanship with Implications for the Analysis of U.S
 Elections.’’ Journal of Politics
, 2008
"... Studies of American politics, particularly legislative politics, rely heavily on measures of the partisanship of a district. We develop a measurement model for this concept, estimating partisanship in the absence of electionspecific, shortterm factors, such as nationallevel swings specific to par ..."
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Cited by 17 (3 self)
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Studies of American politics, particularly legislative politics, rely heavily on measures of the partisanship of a district. We develop a measurement model for this concept, estimating partisanship in the absence of electionspecific, shortterm factors, such as nationallevel swings specific to particular elections, incumbency advantage, and homestate effects in presidential elections. We estimate the measurement model using electoral returns and districtlevel demographic characteristics spanning five decades (1952–2000), letting us assess how the distribution of district partisanship has changed over time, in response to population movements and redistricting, particularly via the creation of majorityminority districts. We validate the partisanship measure with an analysis of congressional rollcall data. The model is easily extended to incorporate other indicators of district partisanship, such as survey data. Almost all empirical studies of congressionalelections rely on a measure of district partisanship, be they studies of incumbency advantage (e.g., Gelman and King 1990), challenger effects (e.g., Jacobson and Kernell 1983), redistricting (e.g., Cox and Katz 1999), regional differences in the electorate, or national forces in elections (e.g., Kawato 1987). These analyses share a common methodological strategy: estimating the effects of more or
The hidden life of latent variables: Bayesian learning with mixed graph models
, 2008
"... Directed acyclic graphs (DAGs) have been widely used as a representation of conditional independence in machine learning and statistics. Moreover, hidden or latent variables are often an important component of graphical models. However, DAG models suffer from an important limitation: the family of D ..."
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Cited by 14 (4 self)
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Directed acyclic graphs (DAGs) have been widely used as a representation of conditional independence in machine learning and statistics. Moreover, hidden or latent variables are often an important component of graphical models. However, DAG models suffer from an important limitation: the family of DAGs is not closed under marginalization of hidden variables. This means that in general we cannot use a DAG to represent the independencies over a subset of variables in a larger DAG. Directed mixed graphs (DMGs) are a representation that includes DAGs as a special case, and overcomes this limitation. This paper introduces algorithms for performing Bayesian inference in Gaussian and probit DMG models. An important requirement for inference is the characterization of the distribution over parameters of the models. We introduce a new distribution for covariance matrices of Gaussian DMGs. We discuss and illustrate how several Bayesian machine learning tasks can benefit from the principle presented here: the power to model dependencies that are generated from hidden variables, but without necessarily modelling such variables explicitly.
Bayesian semiparametric structural equation models with latent variables
 Psychometrika
, 2010
"... Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions ..."
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Cited by 5 (2 self)
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Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In this article, we propose a broad class of semiparametric Bayesian SEMs, which allow mixed categorical and continuous manifest variables while also allowing the latent variables to have unknown distributions. In order to include typical identifiability restrictions on the latent variable distributions, we rely on centered Dirichlet process (CDP) and CDP mixture (CDPM) models. The CDP will induce a latent class model with an unknown number of classes, while the CDPM will induce a latent trait model with unknown densities for the latent traits. A simple and efficient Markov chain Monte Carlo algorithm is developed for posterior computation, and the methods are illustrated using simulated examples, and several applications.
Bayesian Analysis of Multiway Tables in Association Studies: A Model Comparison Approach
 URL
, 2012
"... We consider the problem of statistical inference on unknown quantities structured as a multiway table. We show that such multiway tables are naturally formed by arranging regression coefficients in complex systems of linear models for association analysis. In genetics and genomics, the resulting two ..."
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Cited by 3 (2 self)
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We consider the problem of statistical inference on unknown quantities structured as a multiway table. We show that such multiway tables are naturally formed by arranging regression coefficients in complex systems of linear models for association analysis. In genetics and genomics, the resulting twoway and threeway tables cover many important applications. Within the Bayesian hierarchical model framework, we define the structure of a multiway table through prior specification. Focusing on model comparison and selection, we derive analytic expressions of Bayes factors and their approximations and discuss their theoretical and computational properties. Finally, we demonstrate the strength of our approach using a genomic application of mapping tissuespecific eQTLs (expression quantitative loci). 1
Gaussian process structural equation models with latent variables
 Proceedings of the 26th Conference on Uncertainty on Artificial Intelligence, UAI
, 2010
"... In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with late ..."
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Cited by 2 (0 self)
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In a variety of disciplines such as social sciences, psychology, medicine and economics, the recorded data are considered to be noisy measurements of latent variables connected by some causal structure. This corresponds to a family of graphical models known as the structural equation model with latent variables. While linear nonGaussian variants have been wellstudied, inference in nonparametric structural equation models is still underdeveloped. We introduce a sparse Gaussian process parameterization that defines a nonlinear structure connecting latent variables, unlike common formulations of Gaussian process latent variable models. The sparse parameterization is given a full Bayesian treatment without compromising Markov chain Monte Carlo efficiency. We compare the stability of the sampling procedure and the predictive ability of the model against the current practice. 1
Identifying Graph Clusters using Variational Inference and links to Covariance Parameterisation
"... Finding clusters of wellconnected nodes in a graph is useful in many domains, including Social Network, Web and molecular interaction analyses. From a computational viewpoint, finding these clusters or graph communities is a difficult problem. We consider the framework of Clique Matrices to decompo ..."
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Finding clusters of wellconnected nodes in a graph is useful in many domains, including Social Network, Web and molecular interaction analyses. From a computational viewpoint, finding these clusters or graph communities is a difficult problem. We consider the framework of Clique Matrices to decompose a graph into a set of possibly overlapping clusters, defined as wellconnected subsets of vertices. The decomposition is based on a statistical description which encourages clusters to be well connected and few in number. The formal intractability of inferring the clusters is addressed using a variational approximation which has links to meanfield theories in statistical mechanics. Clique matrices also play a natural role in parameterising positive definite matrices under zero constraints on elements of the matrix. We show that clique matrices can parameterise all positive definite matrices restricted according to a decomposable graph and form a structured Factor Analysis approximation in the nondecomposable case.
DATA AnALYSIS TECHnIQUES In SERVICE QUALITY LITERATURE: ESSEnTIALS AnD ADVAnCES
, 2013
"... Academic and business researchers have for long debated on the most appropriate data analysis techniques that can be employed in conducting empirical researches in the domain of services marketing. An exhaustive review of selected empirical studies ranging ..."
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Academic and business researchers have for long debated on the most appropriate data analysis techniques that can be employed in conducting empirical researches in the domain of services marketing. An exhaustive review of selected empirical studies ranging
Latent Factor Regression Models for Grouped Outcomes
"... Summary: We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious ra ..."
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Summary: We consider regression models for multiple correlated outcomes, where the outcomes are nested in domains. We show that random effect models for this nested situation fit into a standard factor model framework, which leads us to view the modeling options as a spectrum between parsimonious random effect multiple outcomes models and more general continuous latent factor models. We introduce a set of identifiable models along this spectrum that extend an existing random effect model for multiple outcomes nested in domains. We characterize the tradeoffs between parsimony and flexibility in this set of models, applying them to both simulated data and data relating sexually dimorphic traits in male infants to explanatory variables. Supplementary material is available in an online appendix.
Estimation of the Effects of Parental Measures on Child Aggression Using Structural Equation Modeling
, 2012
"... This Selected Project is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact scholarsarchive@byu.edu. ..."
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This Selected Project is brought to you for free and open access by BYU ScholarsArchive. It has been accepted for inclusion in All Theses and Dissertations by an authorized administrator of BYU ScholarsArchive. For more information, please contact scholarsarchive@byu.edu.
Thinning Measurement Models and Questionnaire Design
"... Inferring key unobservable features of individuals is an important task in the applied sciences. In particular, an important source of data in fields such as marketing, social sciences and medicine is questionnaires: answers in such questionnaires are noisy measures of target unobserved features. Wh ..."
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Inferring key unobservable features of individuals is an important task in the applied sciences. In particular, an important source of data in fields such as marketing, social sciences and medicine is questionnaires: answers in such questionnaires are noisy measures of target unobserved features. While comprehensive surveys help to better estimate the latent variables of interest, aiming at a high number of questions comes at a price: refusal to participate in surveys can go up, as well as the rate of missing data; quality of answers can decline; costs associated with applying such questionnaires can also increase. In this paper, we cast the problem of refining existing models for questionnaire data as follows: solve a constrained optimization problem of preserving the maximum amount of information found in a latent variable model using only a subset of existing questions. The goal is to find an optimal subset of a given size. For that, we first define an information theoretical measure for quantifying the quality of a reduced questionnaire. Three different approximate inference methods are introduced to solve this problem. Comparisons against a simple but powerful heuristic are presented. 1