Peter Radchenko
YOU?
Author Swipe
View article: Large Scale Partial Correlation Screening with Uncertainty Quantification
Large Scale Partial Correlation Screening with Uncertainty Quantification Open
Identifying multivariate dependencies in high-dimensional data is an important problem in large-scale inference. This problem has motivated recent advances in mining (partial) correlations, which focus on the challenging ultra-high dimensi…
View article: Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives
Extracting Interpretable Models from Tree Ensembles: Computational and Statistical Perspectives Open
Tree ensembles are non-parametric methods widely recognized for their accuracy and ability to capture complex interactions. While these models excel at prediction, they are difficult to interpret and may fail to uncover useful relationship…
View article: Interlinkage between health workforce availability and socioeconomic status in rural and remote Australia
Interlinkage between health workforce availability and socioeconomic status in rural and remote Australia Open
Introduction Australians living in rural and remote areas experience a higher burden of disease compared to their urban counterparts, whilst having poorer access to essential health services. Socioeconomic status and health workforce short…
View article: Ask for More Than Bayes Optimal: A Theory of Indecisions for Classification
Ask for More Than Bayes Optimal: A Theory of Indecisions for Classification Open
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 high. Given a target accuracy, our goal is to minimize indeci…
View article: Change-Point Detection in Time Series Using Mixed Integer Programming
Change-Point Detection in Time Series Using Mixed Integer Programming Open
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 framework is constructed based on the least squares problem subject…
View article: Predicting Census Survey Response Rates via Interpretable Nonparametric Additive Models with Structured Interactions.
Predicting Census Survey Response Rates via Interpretable Nonparametric Additive Models with Structured Interactions. Open
Accurate and interpretable prediction of survey response rates is important from an operational standpoint. The US Census Bureau's well-known ROAM application uses principled statistical models trained on the US Census Planning Database da…
View article: Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions
Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions Open
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 Census Bureau's well-known ROAM application, which uses a linear…
View article: Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives
Grouped Variable Selection with Discrete Optimization: Computational and Statistical Perspectives Open
We present a new algorithmic framework for grouped variable selection that is based on discrete mathematical optimization. While there exist several appealing approaches based on convex relaxations and nonconvex heuristics, we focus on opt…
View article: Irrational Exuberance: Correcting Bias in Probability Estimates
Irrational Exuberance: Correcting Bias in Probability Estimates Open
We consider the common setting where one observes probability estimates for a large number of events, such as default risks for numerous bonds. Unfortunately, even with unbiased estimates, selecting events corresponding to the most extreme…
View article: Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low
Subset Selection with Shrinkage: Sparse Linear Modeling when the SNR is low Open
We study a seemingly unexpected and relatively less understood overfitting aspect of a fundamental tool in sparse linear modeling - best subset selection, which minimizes the residual sum of squares subject to a constraint on the number of…
View article: Feature Screening in Large Scale Cluster Analysis
Feature Screening in Large Scale Cluster Analysis Open
We propose a novel methodology for feature screening in clustering massive datasets, in which both the number of features and the number of observations can potentially be very large. Taking advantage of a fusion penalization based convex …
View article: The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization Open
We propose a novel high-dimensional linear regression estimator: the Discrete Dantzig Selector, which minimizes the number of nonzero regression coefficients subject to a budget on the maximal absolute correlation between the features and …
View article: Functional additive regression
Functional additive regression Open
We suggest a new method, called Functional Additive Regression, or FAR, for\nefficiently performing high-dimensional functional regression. FAR extends the\nusual linear regression model involving a functional predictor, $X(t)$, and a\nsca…
View article: Index Models for Sparsely Sampled Functional Data
Index Models for Sparsely Sampled Functional Data Open
The regression problem involving functional predictors has many important applications and a number of functional regression methods have been developed. However, a common complication in functional data analysis is one of sparsely observe…
View article: Index Models for Sparsely Sampled Functional Data
Index Models for Sparsely Sampled Functional Data Open
The regression problem involving functional predictors has many important applications and a number of functional regression methods have been developed. However, a common complication in functional data analysis is one of sparsely observe…
View article: Index Models for Sparsely Sampled Functional Data
Index Models for Sparsely Sampled Functional Data Open
The regression problem involving functional predictors has many important applications and a number of functional regression methods have been developed. However, a common complication in functional data analysis is one of sparsely observe…