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05/30/2023
Predicting Rare Events by Shrinking Towards Proportional Odds
Training classifiers is difficult with severe class imbalance, but many ...
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03/22/2023
Generalized Data Thinning Using Sufficient Statistics
Our goal is to develop a general strategy to decompose a random variable...
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11/02/2022
Inferring independent sets of Gaussian variables after thresholding correlations
We consider testing whether a set of Gaussian variables, selected from t...
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10/30/2022
Prediction Sets for High-Dimensional Mixture of Experts Models
Large datasets make it possible to build predictive models that can capt...
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06/03/2022
Interactive Exploration of Large Dendrograms with Prototypes
Hierarchical clustering is one of the standard methods taught for identi...
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01/28/2022
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term bu...
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01/03/2022
Cluster Stability Selection
Stability selection (Meinshausen and Buhlmann, 2010) makes any feature s...
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11/16/2021
Ocean Mover's Distance: Using Optimal Transport for Analyzing Oceanographic Data
Modern ocean datasets are large, multi-dimensional, and inherently spati...
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08/11/2021
Controlling the False Split Rate in Tree-Based Aggregation
In many domains, data measurements can naturally be associated with the ...
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01/29/2021
Tree-based Node Aggregation in Sparse Graphical Models
High-dimensional graphical models are often estimated using regularizati...
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12/05/2020
Selective Inference for Hierarchical Clustering
Testing for a difference in means between two groups is fundamental to a...
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08/25/2020
Modeling Cell Populations Measured By Flow Cytometry With Covariates Using Sparse Mixture of Regressions
The ocean is filled with microscopic microalgae called phytoplankton, wh...
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10/08/2019
Controlling Costs: Feature Selection on a Budget
The traditional framework for feature selection treats all features as c...
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09/25/2019
Testing for Association in Multi-View Network Data
In this paper, we consider data consisting of multiple networks, each co...
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07/19/2019
Reluctant Interaction Modeling
Including pairwise interactions between the predictors of a regression m...
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01/12/2019
Are Clusterings of Multiple Data Views Independent?
In the Pioneer 100 (P100) Wellness Project (Price and others, 2017), mul...
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03/18/2018
Rare Feature Selection in High Dimensions
It is common in modern prediction problems for many predictor variables ...
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01/04/2018
Prediction Error Bounds for Linear Regression With the TREX
The TREX is a recently introduced approach to sparse linear regression. ...
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12/06/2017
Estimating the error variance in a high-dimensional linear model
The lasso has been studied extensively as a tool for estimating the coef...
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11/29/2017
Valid Inference Corrected for Outlier Removal
Ordinary least square (OLS) estimation of a linear regression model is w...
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11/09/2017
Interpretable Vector AutoRegressions with Exogenous Time Series
The Vector AutoRegressive (VAR) model is fundamental to the study of mul...
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06/30/2016
The Simulator: An Engine to Streamline Simulations
The simulator is an R package that streamlines the process of performing...
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06/01/2016
Graph-Guided Banding of the Covariance Matrix
Regularization has become a primary tool for developing reliable estimat...
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04/25/2016
Learning Local Dependence In Ordered Data
In many applications, data come with a natural ordering. This ordering c...
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04/22/2016
Non-convex Global Minimization and False Discovery Rate Control for the TREX
The TREX is a recently introduced method for performing sparse high-dime...
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12/05/2015
Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations
Demanding sparsity in estimated models has become a routine practice in ...
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12/17/2014
Hierarchical Vector Autoregression
Vector autoregression (VAR) is a fundamental tool for modeling the joint...
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07/17/2014
Sparse Partially Linear Additive Models
The generalized partially linear additive model (GPLAM) is a flexible an...
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05/23/2014
Convex Banding of the Covariance Matrix
We introduce a new sparse estimator of the covariance matrix for high-di...
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05/22/2012
A lasso for hierarchical interactions
We add a set of convex constraints to the lasso to produce sparse intera...
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11/09/2010
Strong rules for discarding predictors in lasso-type problems
We consider rules for discarding predictors in lasso regression and rela...
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11/01/2010
