V
Victor Chernozhukov
Researcher at Massachusetts Institute of Technology
Publications - 374
Citations - 25283
Victor Chernozhukov is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Estimator & Quantile. The author has an hindex of 73, co-authored 370 publications receiving 20588 citations. Previous affiliations of Victor Chernozhukov include Amazon.com & New Economic School.
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Valid Post-Selection Inference in High-Dimensional Approximately Sparse Quantile Regression Models
TL;DR: This paper proposed new inference methods for a regression coefficient of interest in a (heterogeneous) quantile regression model, where the number of regressors potentially exceeds the sample size but a subset of them suffice to construct a reasonable approximation to the conditional quantile function.
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On cross-validated Lasso in high dimensions
TL;DR: This paper derives non-asymptotic error bounds for the Lasso estimator when the penalty parameter for the estimator is chosen using $K$-fold cross-validation and serves as a justification for the widely spread practice of using cross- validation as a method to choose the Penalty parameter.
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Rearranging Edgeworth–Cornish–Fisher expansions
TL;DR: In this paper, the authors apply a regularization procedure called increasing rearrangement to monotonize Edgeworth and Cornish-Fisher expansions and any other related approximations of distribution and quantile functions of sample statistics.
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Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings
TL;DR: In this paper, Gine and Nickl derived 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 bootstrap processes.
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Conditional quantile processes based on series or many regressors
TL;DR: In this paper, the authors develop a nonparametric QR-series framework, covering many regressors as a special case, for performing inference on the entire conditional quantile function and its linear functionals.