5 papers
Modeling with Categorical Features via Exact Fusion and Sparsity Regularisation
Kayhan Behdin, Riade Benbaki, Peter Radchenko +1
We study the high-dimensional linear regression problem with categorical predictors that have many levels. We propose a new estimation approach, which performs model compression vi…
Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing
Mohamed Ndaoud, Peter Radchenko, Bradley Rava
Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is hi…
Large Scale Partial Correlation Screening with Uncertainty Quantification
Emily Neo, Peter Radchenko, Bala Rajaratnam
Identifying multivariate dependencies in high-dimensional data is an important problem in large-scale inference. This problem has motivated recent advances in mining (partial) corr…
Change-Point Detection in Time Series Using Mixed Integer Programming
Artem Prokhorov, Peter Radchenko, Alexander Semenov +1
We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framewor…
Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions
Shibal Ibrahim, Peter Radchenko, Emanuel Ben-David +1
In this paper, we consider the problem of predicting survey response rates using a family of flexible and interpretable nonparametric models. The study is motivated by the US Censu…