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Abel Rodriguez


Professor, Statistics

Research Interests

Bayesian Statistics, Nonparametric Methods, Latent Variable Models, Spatial Statistics


I am a Professor of Statistics at the University of Washington, and an affiliate member of the eScience Institute and the Center for Statistics in the Social Sciences.  Currently, I also serve as the Chair of the Department of Statistics.  I develop statistical methods for complex problems in biology, social sciences, and engineering. My research interests include Bayesian statistics and machine learning, especially nonparametric methods, spatio-temporal models, network analysis and extreme value theory.


Explaining Differences in Voting Patterns Across Voting Domains Using Hierarchical Bayesian Models
Erin Lipman, Scott Moser, Abel Rodriguez
Spatial voting models of legislators' preferences are used in political science to test theories about their voting behavior. These models posit that…

A Novel Class of Unfolding Models for Binary Preference Data
Rayleigh Lei, Abel Rodriguez
We develop a new class of spatial voting models for binary preference data that can accommodate both monotonic and non-monotonic response functions, and are…

Dynamic Factor Models for Binary Data in Circular Spaces: An Application to the U.S. Supreme Court
Rayleigh Lei, Abel Rodriguez
Latent factor models are widely used in the social and behavioral science as scaling tools to map discrete multivariate outcomes into low dimensional,…

On Data Analysis Pipelines and Modular Bayesian Modeling
Erin Lipman, Abel Rodriguez
The most common approach to implementing data analysis pipelines involves obtaining point estimates from the upstream modules and then treating these as known…

Improvements on Scalable Stochastic Bayesian Inference Methods for Multivariate Hawkes Process
Alex Ziyu Jiang, Abel Rodríguez
Multivariate Hawkes Processes (MHPs) are a class of point processes that can account for complex temporal dynamics among event sequences. In this work, we…

A Bayesian Approach for De-duplication in the Presence of Relational Data
Juan Sosa, Abel Rodriguez
In this paper, we study the impact of combining profile and network data in a de-duplication setting. We also assess the influence of a range of prior…

Laplace Power-expected-posterior priors for generalized linear models with applications to logistic regression
Anupreet Porwal, Abel Rodriguez
Power-expected-posterior (PEP) methodology, which borrows ideas from the literature on power priors, expected-posterior priors and unit information priors,…

A Prior for Record Linkage Based on Allelic Partitions
Brenda Betancourt, Juan Sosa, Abel Rodríguez
In database management, record linkage aims to identify multiple records that correspond to the same individual. This task can be treated as a clustering…

A Latent Space Model for Cognitive Social Structures Data
Juan Sosa, Abel Rodriguez
This paper introduces a novel approach for modeling a set of directed, binary networks in the context of cognitive social structures (CSSs) data. We adopt a…

High Dimensional Bayesian Network Classification with Network Global-Local Shrinkage Priors
Sharmistha Guha, Abel Rodriguez
This article proposes a novel Bayesian classification framework for networks with labeled nodes. While literature on statistical modeling of network data…

A Bayesian Approach to Spherical Factor Analysis for Binary Data
Xingchen Yu, Abel Rodriguez
Factor models are widely used across diverse areas of application for purposes that include dimensionality reduction, covariance estimation, and feature…

A Record Linkage Model Incorporating Relational Data
Juan Sosa, Abel Rodriguez
In this paper we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different…

Bayesian Regression with Undirected Network Predictors with an Application to Brain Connectome Data
Sharmistha Guha, Abel Rodriguez
This article proposes a Bayesian approach to regression with a continuous scalar response and an undirected network predictor. Undirected network predictors…

Latent nested nonparametric priors
Federico Camerlenghi, David B. Dunson, Antonio Lijoi, Igor PrÜnster, Abel Rodríguez
Discrete random structures are important tools in Bayesian nonparametrics and the resulting models have proven effective in density estimation, clustering,…

Modelling and prediction of financial trading networks: An application to the NYMEX natural gas futures market
Brenda Betancourt, Abel Rodríguez, Naomi Boyd
Over the last few years there has been a growing interest in using financial trading networks to understand the microstructure of financial markets. Most of…

Investigating Competition in Financial Markets: A Sparse Autologistic Model for Dynamic Network Data
Brenda Betancourt, Abel Rodríguez, Naomi Boyd
We develop a sparse autologistic model for investigating the impact of diversification and disintermediation strategies in the evolution of financial trading…

Bayesian Fused Lasso regression for dynamic binary networks
Brenda Betancourt, Abel Rodríguez, Naomi Boyd
We propose a multinomial logistic regression model for link prediction in a time series of directed binary networks. To account for the dynamic nature of the…

Stochastic blockmodels for exchangeable collections of networks
Perla Reyes, Abel Rodriguez
We construct a novel class of stochastic blockmodels using Bayesian nonparametric mixtures. These model allows us to jointly estimate the structure of multiple…

Default Bayesian Analysis for the Multivariate Ewens Distribution
Abel Rodriguez
We derive the Jeffreys prior for the parameter of the Multivariate Ewens Distribution and study some of its properties. In particular, we show that this prior…

Sparse covariance estimation in heterogeneous samples
Abel Rodriguez, Alex Lenkoski, Adrian Dobra
Standard Gaussian graphical models (GGMs) implicitly assume that the conditional independence among variables is common to all observations in the sample…