714 citations · 1.1k across the 12 of their papers we have counts for
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Bayesian model choice and information criteria in sparse generalized linear models
Rina Foygel, Mathias Drton
We consider Bayesian model selection in generalized linear models that are high-dimensional, with the number of covariates p being large relative to the sample size n, but sparse i…
Fast-rate and optimistic-rate error bounds for L1-regularized regression
Rina Foygel, Nathan Srebro
We consider the prediction error of linear regression with L1 regularization when the number of covariates p is large relative to the sample size n. When the model is k-sparse and…
Half-trek criterion for generic identifiability of linear structural equation models
Rina Foygel, Jan Draisma, Mathias Drton
A linear structural equation model relates random variables of interest and corresponding Gaussian noise terms via a linear equation system. Each such model can be represented by a…
Extended Bayesian Information Criteria for Gaussian Graphical Models
Rina Foygel, Mathias Drton
Gaussian graphical models with sparsity in the inverse covariance matrix are of significant interest in many modern applications. For the problem of recovering the graphical struct…
Global identifiability of linear structural equation models
Mathias Drton, Rina Foygel, Seth Sullivant
Structural equation models are multivariate statistical models that are defined by specifying noisy functional relationships among random variables. We consider the classical case…