166 citations · 595 across the 71 of their papers we have counts for
16 papers · 1 filter
Improved Variance Estimation in Homoskedastic Nonparametric Random-Design Regression via a Two-Scale Approach
Edgar Dobriban, Rajarshi Mukherjee, James M. Robins +1
We study estimation of a constant conditional variance in nonparametric regression with a -dimensional random design. This is an important problem, and similar questions a…
Sharp Minimax Theory for Randomized Experiments
Timothy Sudijono, Edgar Dobriban, Eric Tchetgen Tchetgen
We study minimax-optimal designs and estimators for estimating the sample average treatment effect in finite population randomized experiments, where both design and estimator are…
The Benjamini--Hochberg Procedure Can Fail to Control the FDR for Correlated Two-Sided Gaussian Tests
Edgar Dobriban
We show that the Benjamini--Hochberg procedure can fail to control the false discovery rate (FDR) at its nominal level for correlated two-sided Gaussian -values. We construct a…
Inference in Randomized Least Squares and PCA via Normality of Quadratic Forms
Leda Wang, Zhixiang Zhang, Edgar Dobriban
Randomized algorithms can be used to speed up the analysis of large datasets. In this paper, we develop a unified methodology for statistical inference via randomized sketching or…
Comparing Classes of Estimators: When does Gradient Descent Beat Ridge Regression in Linear Models?
Dominic Richards, Edgar Dobriban, Patrick Rebeschini
Methods for learning from data depend on various types of tuning parameters, such as penalization strength or step size. Since performance can depend strongly on these parameters,…
Consistency of invariance-based randomization tests
Edgar Dobriban
Invariance-based randomization tests -- such as permutation tests, rotation tests, or sign changes -- are an important and widely used class of statistical methods. They allow draw…
Selecting the number of components in PCA via random signflips
David Hong, Yue Sheng, Edgar Dobriban
Principal component analysis (PCA) is a foundational tool in modern data analysis, and a crucial step in PCA is selecting the number of components to keep. However, classical selec…
How to reduce dimension with PCA and random projections?
Fan Yang, Sifan Liu, Edgar Dobriban +1
In our "big data" age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension red…
Ridge Regression: Structure, Cross-Validation, and Sketching
Sifan Liu, Edgar Dobriban
We study the following three fundamental problems about ridge regression: (1) what is the structure of the estimator? (2) how to correctly use cross-validation to choose the regula…
WONDER: Weighted one-shot distributed ridge regression in high dimensions
Edgar Dobriban, Yue Sheng
In many areas, practitioners need to analyze large datasets that challenge conventional single-machine computing. To scale up data analysis, distributed and parallel computing appr…
Distributed linear regression by averaging
Edgar Dobriban, Yue Sheng
Distributed statistical learning problems arise commonly when dealing with large datasets. In this setup, datasets are partitioned over machines, which compute locally, and communi…
Robust Inference Under Heteroskedasticity via the Hadamard Estimator
Edgar Dobriban, Weijie J. Su, Yachong Yang +1
Drawing statistical inferences from large datasets in a model-robust way is an important problem in statistics and data science. In this paper, we propose methods that are robust t…
Permutation methods for factor analysis and PCA
Edgar Dobriban
Researchers often have datasets measuring features of samples, such as test scores of students. In factor analysis and PCA, these features are thought to be influenced by…
Sharp detection in PCA under correlations: all eigenvalues matter
Edgar Dobriban
Principal component analysis (PCA) is a widely used method for dimension reduction. In high dimensional data, the "signal" eigenvalues corresponding to weak principal components (P…
High-Dimensional Asymptotics of Prediction: Ridge Regression and Classification
Edgar Dobriban, Stefan Wager
We provide a unified analysis of the predictive risk of ridge regression and regularized discriminant analysis in a dense random effects model. We work in a high-dimensional asympt…
Regularity Properties for Sparse Regression
Edgar Dobriban, Jianqing Fan
Statistical and machine learning theory has developed several conditions ensuring that popular estimators such as the Lasso or the Dantzig selector perform well in high-dimensional…