3 papers
stat.ME2020
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…
stat.ME2019
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…
stat.AP2018
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…