8 citations · 13 across the 9 of their papers we have counts for
11 papers
Bayesian Nonparametric Causal Inference for High-Dimensional Nutritional Data via Factor-Based Exposure Mapping
Dafne Zorzetto, Zizhao Xie, Julian Stamp +2
Diet plays a crucial role in health, and understanding the causal effects of dietary patterns is essential for informing public health policy and personalized nutrition strategies.…
Bayesian integrative factor analysis methods, with application in nutrition and genomics data
Mavis Liang, Blake Hansen, Alejandra Avalos-Pacheco +1
High-dimensional data are crucial in biomedical research. Integrating such data from multiple studies is a critical process that relies on the choice of advanced statistical models…
Multivariate Causal Effects: a Bayesian Causal Regression Factor Model
Dafne Zorzetto, Jenna Landy, Corwin Zigler +2
The impact of wildfire smoke on air quality is a growing concern, contributing to air pollution through a complex mixture of chemical species with important implications for public…
Sparse Bayesian Factor Models with Mass-Nonlocal Factor Scores
Yingjie Huang, Dafne Zorzetto, Roberta De Vito
Bayesian factor models are widely used for dimensionality reduction and pattern discovery in high-dimensional datasets across diverse fields. These models typically focus on imposi…
Fast Variational Inference for Bayesian Factor Analysis in Single and Multi-Study Settings
Blake Hansen, Alejandra Avalos-Pacheco, Massimiliano Russo +1
Factors models are routinely used to analyze high-dimensional data in both single-study and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte Ca…
Multi-study factor regression model: an application in nutritional epidemiology
Roberta De Vito, Alejandra Avalos-Pacheco
Diet is a risk factor for many diseases. In nutritional epidemiology, studying reproducible dietary patterns is critical to reveal important associations with health. However, it i…
Estimating Gaussian graphical models of multi-study data with Multi-Study Factor Analysis
Katherine H. Shutta, Denise M. Scholtens, William L. Lowe +2
Network models are powerful tools for gaining new insights from complex biological data. Most lines of investigation in biology involve comparing datasets in the setting where the…
Bayesian Combinatorial Multi-Study Factor Analysis
Isabella N. Grabski, Roberta De Vito, Lorenzo Trippa +1
Analyzing multiple studies allows leveraging data from a range of sources and populations, but until recently, there have been limited methodologies to approach the joint unsupervi…
Bayesian Ordinal Quantile Regression with a Partially Collapsed Gibbs Sampler
Isabella N Grabski, Roberta De Vito, Barbara E Engelhardt
Unlike standard linear regression, quantile regression captures the relationship between covariates and the conditional response distribution as a whole, rather than only the relat…
Bayesian Multi-study Factor Analysis for High-throughput Biological Data
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa +1
This paper presents a new modeling strategy for joint unsupervised analysis of multiple high-throughput biological studies. As in Multi-study Factor Analysis, our goals are to iden…
Multi-study Factor Analysis
Roberta De Vito, Ruggero Bellio, Lorenzo Trippa +1
We introduce a novel class of factor analysis methodologies for the joint analysis of multiple studies. The goal is to separately identify and estimate 1) common factors shared acr…