5 papers
Causal Stability Selection
Falco J. Bargagli-Stoffi, Omar Melikechi
Identifying covariates that modify treatment effects is a central problem in causal inference. Yet existing data-adaptive procedures do not provide finite-sample control over the e…
Local graph estimation with pathwise false discovery control
Omar Melikechi, David B. Dunson, Noureddine Melikechi +1
Many datasets include a small set of variables, such as biomarkers or clinical outcomes, whose relationships to the broader system are of primary scientific interest. Estimating th…
Sequential Gibbs Posteriors with Applications to Principal Component Analysis
Steven Winter, Omar Melikechi, David B. Dunson
Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to th…
Nonparametric IPSS: Fast, flexible feature selection with false discovery control
Omar Melikechi, David B. Dunson, Jeffrey W. Miller
Feature selection is a critical task in machine learning and statistics. However, existing feature selection methods either (i) rely on parametric methods such as linear or general…
Integrated path stability selection
Omar Melikechi, Jeffrey W. Miller
Stability selection is a popular method for improving feature selection algorithms. One of its key attributes is that it provides theoretical upper bounds on the expected number of…