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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Intersection Bounds: Estimation and Inference
TL;DR: A practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set is developed.
Journal ArticleDOI
An MCMC Approach to Classical Estimation
Victor Chernozhukov,Han Hong +1 more
TL;DR: The Laplace type estimators (LTE) as discussed by the authors are derived from quasi-posterior distributions defined as transformations of general (non-likelihood-based) statistical criterion functions, such as those in GMM, nonlinear IV, empirical likelihood, and minimum distance methods.
ReportDOI
Gaussian approximation of suprema of empirical processes
TL;DR: An abstract approximation theorem that is applicable to a wide variety of problems, primarily in statistics, is proved and the bound in the main approximation theorem is non-asymptotic and the theorem does not require uniform boundedness of the class of functions.
Posted Content
Central Limit Theorems and Bootstrap in High Dimensions
TL;DR: In this paper, the central limit and bootstrap theorems for probabilities that sums of centered high-dimensional random vectors hit hyperrectangles and sparsely convex sets are derived.
ReportDOI
Intersection Bounds: estimation and inference
TL;DR: This work develops a practical and novel method for inference on intersection bounds, namely bounds defined by either the infimum or supremum of a parametric or nonparametric function, or equivalently, the value of a linear programming problem with a potentially infinite constraint set.