1 citations · 1 across the 6 of their papers we have counts for
6 papers
Reduced-rank Generalized Bilinear Models
Kevin S. Kapner, Jeffrey W. Miller
Dimensionality reduction and effect estimation are central tasks in the analysis of high-dimensional data such as in genomics. Generalized bilinear models (GBMs) provide a versatil…
A powerful goodness-of-fit test using the probability integral transform of order statistics
Christian T. Covington, Jeffrey W. Miller
Goodness-of-fit (GoF) tests are a fundamental component of statistical practice, essential for checking model assumptions and testing scientific hypotheses. Despite their widesprea…
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…
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…
Mixture models with a prior on the number of components
Jeffrey W. Miller, Matthew T. Harrison
A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with Dirichlet weights, and put a prior on the number…