6 citations · 20 across the 17 of their papers we have counts for
19 papers
Bayesian Analysis Using a Constrained Mixture of Normal-Inverse-Gamma Models
Madelyn Clinch, Jonathan R. Bradley, Andrés F. Barrientos +1
Gaussian mixtures of regressions are commonly implemented via a Gibbs sampler. This Markov chain Monte Carlo (MCMC) algorithm can be computationally burdensome because of the need…
Bayesian nonparametric modeling of mixed-type bounded data
Rufeng Liu, Claudia Wehrhahn, Andrés F. Barrientos +1
We propose a Bayesian nonparametric model for mixed-type bounded data, where some variables are compositional and others are interval-bounded. Compositional variables are non-negat…
Nonparametric Multivariate Profile Monitoring Via Tree Ensembles
Daniel A. Timme, Andrés F. Barrientos, Eric Chicken +1
Monitoring random profiles over time is used to assess whether the system of interest, generating the profiles, is operating under desired conditions at any time-point. In practice…
Incompatibilities Between Current Practices in Statistical Data Analysis and Differential Privacy
Joshua Snoke, Claire McKay Bowen, Aaron R. Williams +1
The authors discuss their experience applying differential privacy with a complex data set with the goal of enabling standard approaches to statistical data analysis. They highligh…
Differentially Private Methods for Compositional Data
Qi Guo, Andrés F. Barrientos, Víctor Peña
Confidential data, such as electronic health records, activity data from wearable devices, and geolocation data, are becoming increasingly prevalent. Differential privacy provides…
Differentially Private Hypothesis Testing with the Subsampled and Aggregated Randomized Response Mechanism
Víctor Peña, Andrés F. Barrientos
Randomized response is one of the oldest and most well-known methods for analyzing confidential data. However, its utility for differentially private hypothesis testing is limited…
Nonparametric Bayesian Approach to Treatment Ranking in Network Meta-Analysis with Application to Comparisons of Antidepressants
Andrés F. Barrientos, Garritt L. Page, Lifeng Lin
Network meta-analysis is a powerful tool to synthesize evidence from independent studies and compare multiple treatments simultaneously. A critical task of performing a network met…
Profile Monitoring via Eigenvector Perturbation
Takayuki Iguchi, Andrés F. Barrientos, Eric Chicken +1
In Statistical Process Control, control charts are often used to detect undesirable behavior of sequentially observed quality characteristics. Designing a control chart with desira…
A Latent Class Modeling Approach for Generating Synthetic Data and Making Posterior Inferences from Differentially Private Counts
Michelle Pistner Nixon, Andrés F. Barrientos, Jerome P. Reiter +1
Several algorithms exist for creating differentially private counts from contingency tables, such as two-way or three-way marginal counts. The resulting noisy counts generally do n…
A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data
Andrés F. Barrientos, Aaron R. Williams, Joshua Snoke +1
Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few…
Mixture representations and Bayesian nonparametric inference for likelihood ratio ordered distributions
Michael Jauch, Andrés F. Barrientos, Víctor Peña +1
In this article, we introduce mixture representations for likelihood ratio ordered distributions. Essentially, the ratio of two probability densities, or mass functions, is monoton…
Differentially private methods for managing model uncertainty in linear regression models
Víctor Peña, Andrés F. Barrientos
In this work, we propose differentially private methods for hypothesis testing, model averaging, and model selection for normal linear models. We consider Bayesian methods based on…
Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials
Claudia Wehrhahn, Andrés F. Barrientos, Alejandro Jara
We discuss Bayesian nonparametric procedures for the regression analysis of compositional responses, that is, data supported on a multivariate simplex. The procedures are based on…
Bayesian inferences on uncertain ranks and orderings: Application to ranking players and lineups
Andres F. Barrientos, Deborshee Sen, Garritt L Page +1
It is common to be interested in rankings or order relationships among entities. In complex settings where one does not directly measure a univariate statistic upon which to base r…
Simultaneous Edit and Imputation for Household Data with Structural Zeros
Olanrewaju Akande, Andrés Barrientos, Jerome P. Reiter
Multivariate categorical data nested within households often include reported values that fail edit constraints---for example, a participating household reports a child's age as ol…
Multiple Imputation of Missing Values in Household Data with Structural Zeros
Olanrewaju Akande, Jerome Reiter, Andrés F. Barrientos
We present an approach for imputation of missing items in multivariate categorical data nested within households. The approach relies on a latent class model that (i) allows for ho…
Bayesian Bootstraps for Massive Data
Andrés F. Barrientos, Víctor Peña
In this article, we present data-subsetting algorithms that allow for the approximate and scalable implementation of the Bayesian bootstrap. They are analogous to two existing algo…
Differentially private significance tests for regression coefficients
Andrés F. Barrientos, Jerome P. Reiter, Ashwin Machanavajjhala +1
Many data producers seek to provide users access to confidential data without unduly compromising data subjects' privacy and confidentiality. One general strategy is to require use…
Providing Access to Confidential Research Data Through Synthesis and Verification: An Application to Data on Employees of the U.S. Federal Government
Andrés F. Barrientos, Alexander Bolton, Tom Balmat +6
Data stewards seeking to provide access to large-scale social science data face a difficult challenge. They have to share data in ways that protect privacy and confidentiality, are…