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Overfitted high-dimensional matrix factorizations via adaptive spectral shrinkage
Lorenzo Mauri, David B. Dunson
Factor models are popular approaches for analyzing high-dimensional data to extract low-rank signals and estimate covariances. They decompose the covariance matrix as the sum of lo…
Inference on covariance structure in high-dimensional multi-view data
Lorenzo Mauri, David B. Dunson
This article focuses on covariance estimation for multi-view data. Popular approaches rely on factor-analytic decompositions that have shared and view-specific latent factors. Post…
Pathway-based Bayesian factor models for 'omics data
Lorenzo Mauri, Federica Stolf, Amy H. Herring +2
Interpreting RNA-sequencing data requires identifying coordinated gene expression patterns that correspond to biological pathways. Standard factor models provide useful dimension r…
Spectral decomposition-assisted multi-study factor analysis
Lorenzo Mauri, Niccolò Anceschi, David B. Dunson
This article focuses on covariance estimation for multi-study data. Popular approaches employ factor-analytic terms with shared and study-specific loadings that decompose the varia…
Factor pre-training in Bayesian multivariate logistic models
Lorenzo Mauri, David B. Dunson
This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors…