322 citations · 816 across the 25 of their papers we have counts for
12 papers · 1 filter
High-dimensional Data Bootstrap
Victor Chernozhukov, Denis Chetverikov, Kengo Kato +1
This article reviews recent progress in high-dimensional bootstrap. We first review high-dimensional central limit theorems for distributions of sample mean vectors over the rectan…
Selecting Penalty Parameters of High-Dimensional M-Estimators using Bootstrapping after Cross-Validation
Denis Chetverikov, Jesper Riis-Vestergaard Sørensen
We develop a new method for selecting the penalty parameter for -penalized M-estimators in high dimensions, which we refer to as bootstrapping after cross-validation. We…
Improved Central Limit Theorem and bootstrap approximations in high dimensions
Victor Chernozhukov, Denis Chetverikov, Kengo Kato +1
This paper deals with the Gaussian and bootstrap approximations to the distribution of the max statistic in high dimensions. This statistic takes the form of the maximum over compo…
High-Dimensional Econometrics and Regularized GMM
Alexandre Belloni, Victor Chernozhukov, Denis Chetverikov +2
This chapter presents key concepts and theoretical results for analyzing estimation and inference in high-dimensional models. High-dimensional models are characterized by having a…
Detailed proof of Nazarov's inequality
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
The purpose of this note is to provide a detailed proof of Nazarov's inequality stated in Lemma A.1 in Chernozhukov, Chetverikov, and Kato (2017, Annals of Probability).
On cross-validated Lasso in high dimensions
Denis Chetverikov, Zhipeng Liao, Victor Chernozhukov
In this paper, we derive non-asymptotic error bounds for the Lasso estimator when the penalty parameter for the estimator is chosen using -fold cross-validation. Our bounds impl…
Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
We derive strong approximations to the supremum of the non-centered empirical process indexed by a possibly unbounded VC-type class of functions by the suprema of the Gaussian and…
Central Limit Theorems and Bootstrap in High Dimensions
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
This paper derives central limit and bootstrap theorems for probabilities that sums of centered high-dimensional random vectors hit hyperrectangles and sparsely convex sets. Specif…
Inference on causal and structural parameters using many moment inequalities
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
This paper considers the problem of testing many moment inequalities where the number of moment inequalities, denoted by , is possibly much larger than the sample size . Ther…
Anti-concentration and honest, adaptive confidence bands
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
Modern construction of uniform confidence bands for nonparametric densities (and other functions) often relies on the classical Smirnov-Bickel-Rosenblatt (SBR) condition; see, for…
Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors
Victor Chernozhukov, Denis Chetverikov, Kengo Kato
We derive a Gaussian approximation result for the maximum of a sum of high-dimensional random vectors. Specifically, we establish conditions under which the distribution of the max…
Testing Regression Monotonicity in Econometric Models
Denis Chetverikov
Monotonicity is a key qualitative prediction of a wide array of economic models derived via robust comparative statics. It is therefore important to design effective and practical…