565 citations · 2.9k across the 63 of their papers we have counts for
20 papers · 1 filter
Unbiased Test Error Estimation in the Poisson Means Problem via Coupled Bootstrap Techniques
Natalia L. Oliveira, Jing Lei, Ryan J. Tibshirani
We propose a coupled bootstrap (CB) method for the test error of an arbitrary algorithm that estimates the mean in a Poisson sequence, often called the Poisson means problem. The i…
Permutation tests using arbitrary permutation distributions
Aaditya Ramdas, Rina Foygel Barber, Emmanuel J. Candes +1
Permutation tests date back nearly a century to Fisher's randomized experiments, and remain an immensely popular statistical tool, used for testing hypotheses of independence betwe…
Conformal prediction beyond exchangeability
Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas +1
Conformal prediction is a popular, modern technique for providing valid predictive inference for arbitrary machine learning models. Its validity relies on the assumptions of exchan…
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States
Evan L. Ray, Logan C. Brooks, Jacob Bien +12
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensembl…
Modelling High-Dimensional Categorical Data Using Nonconvex Fusion Penalties
Benjamin G. Stokell, Rajen D. Shah, Ryan J. Tibshirani
We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding…
Reluctant Interaction Modeling
Guo Yu, Jacob Bien, Ryan Tibshirani
Including pairwise interactions between the predictors of a regression model can produce better predicting models. However, to fit such interaction models on typical data sets in b…
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights
Maria Jahja, David C. Farrow, Roni Rosenfeld +1
The Kalman filter (KF) is one of the most widely used tools for data assimilation and sequential estimation. In this work, we show that the state estimates from the KF in a standar…
Predictive inference with the jackknife+
Rina Foygel Barber, Emmanuel J. Candes, Aaditya Ramdas +1
This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted…
Conformal Prediction Under Covariate Shift
Ryan J. Tibshirani, Rina Foygel Barber, Emmanuel J. Candes +1
We extend conformal prediction methodology beyond the case of exchangeable data. In particular, we show that a weighted version of conformal prediction can be used to compute distr…
Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data
Sangwon Hyun, Kevin Lin, Max G'Sell +1
Changepoint detection methods are used in many areas of science and engineering, e.g., in the analysis of copy number variation data, to detect abnormalities in copy numbers along…
Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
Trevor Hastie, Robert Tibshirani, Ryan J. Tibshirani
In exciting new work, Bertsimas et al. (2016) showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO)…
From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation
Saharon Rosset, Ryan J. Tibshirani
In statistical prediction, classical approaches for model selection and model evaluation based on covariance penalties are still widely used. Most of the literature on this topic i…
The Multiple Quantile Graphical Model
Alnur Ali, J. Zico Kolter, Ryan J. Tibshirani
We introduce the Multiple Quantile Graphical Model (MQGM), which extends the neighborhood selection approach of Meinshausen and Buhlmann for learning sparse graphical models. The l…
Exact Post-Selection Inference for Changepoint Detection and Other Generalized Lasso Problems
Sangwon Hyun, Max G'Sell, Ryan J. Tibshirani
We study tools for inference conditioned on model selection events that are defined by the generalized lasso regularization path. The generalized lasso estimate is given by the sol…
Approximate Recovery in Changepoint Problems, from Estimation Error Rates
Kevin Lin, James Sharpnack, Alessandro Rinaldo +1
In the 1-dimensional multiple changepoint detection problem, we prove that any procedure with a fast enough error rate, in terms of its estimation of the underlying piecew…
Distribution-Free Predictive Inference For Regression
Jing Lei, Max G'Sell, Alessandro Rinaldo +2
We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a predic…
Selective Sequential Model Selection
William Fithian, Jonathan Taylor, Robert Tibshirani +1
Many model selection algorithms produce a path of fits specifying a sequence of increasingly complex models. Given such a sequence and the data used to produce them, we consider th…
Nonparametric modal regression
Yen-Chi Chen, Christopher R. Genovese, Ryan J. Tibshirani +1
Modal regression estimates the local modes of the distribution of given , instead of the mean, as in the usual regression sense, and can hence reveal important structure m…
Exact Post-Selection Inference for Sequential Regression Procedures
Ryan J. Tibshirani, Jonathan Taylor, Richard Lockhart +1
We propose new inference tools for forward stepwise regression, least angle regression, and the lasso. Assuming a Gaussian model for the observation vector y, we first describe a g…
Tests in adaptive regression via the Kac-Rice formula
Jonathan Taylor, Joshua Loftus, Ryan Tibshirani
We derive an exact p-value for testing a global null hypothesis in a general adaptive regression problem. The general approach uses the Kac-Rice formula, as described in (Adler & T…