受强制性开放获取政策约束的文章 - Victor Chernozhukov了解详情
可在其他位置公开访问的文章：35 篇
Double/De-Biased Machine Learning for Treatment and Causal Parameters
V Chernozhukov, D Chetverikov, M Demirer, E Duflo, C Hansen, ...
Econometrics Journal; 2018; arXiv preprint arXiv:1608.00060, 2016
强制性开放获取政策： US National Science Foundation
Double/debiased/neyman machine learning of treatment effects
V Chernozhukov, D Chetverikov, M Demirer, E Duflo, C Hansen, ...
American Economic Review 107 (5), 261-65, 2017
强制性开放获取政策： US National Science Foundation
Locally robust semiparametric estimation
V Chernozhukov, JC Escanciano, H Ichimura, WK Newey, JM Robins
Econometrica 2022; arXiv preprint arXiv:1608.00033, 2022
强制性开放获取政策： Government of Spain
Debiased machine learning of global and local parameters using regularized Riesz representers
V Chernozhukov, WK Newey, R Singh
The Econometrics Journal (ArXiv 2018), 2022
强制性开放获取政策： US National Science Foundation
Monge–Kantorovich depth, quantiles, ranks and signs
V Chernozhukov, A Galichon, M Hallin, M Henry
Annals of Statistics 45 (1), 223-256, 2017
强制性开放获取政策： US National Science Foundation, European Commission
An exact and robust conformal inference method for counterfactual and synthetic controls
V Chernozhukov, K Wüthrich, Y Zhu
Journal of the American Statistical Association, 1-16, 2021
强制性开放获取政策： US National Science Foundation
VECTOR QUANTILE REGRESSION: AN OPTIMAL TRANSPORT APPROACH
G CARLIER, V CHERNOZHUKOV, A GALICHON
The Annals of Statistics, 2016; arXiv preprint arXiv:1406.4643, 2014
强制性开放获取政策： European Commission
On cross-validated lasso in high dimensions
D Chetverikov, Z Liao, V Chernozhukov
The Annals of Statistics 49 (3), 1300-1317, 2021
强制性开放获取政策： US National Science Foundation
Automatic debiased machine learning of causal and structural effects
V Chernozhukov, WK Newey, R Singh
Econometrica 2022; arXiv preprint arXiv:1809.05224, 2022
强制性开放获取政策： US National Science Foundation
Distributional conformal prediction
V Chernozhukov, K Wüthrich, Y Zhu
Proceedings of the National Academy of Science (2021); arXiv preprint arXiv …, 2019
强制性开放获取政策： US National Science Foundation
Improved central limit theorem and bootstrap approximations in high dimensions
V Chernozhukov, D Chetverikov, K Kato, Y Koike
Annals of Statistics 2022; arXiv preprint arXiv:1912.10529, 2022
强制性开放获取政策： US National Science Foundation
Uniformly valid post-regularization confidence regions for many functional parameters in z-estimation framework
A Belloni, V Chernozhukov, D Chetverikov, Y Wei
The Annals of Statistics 46 (6B), 3643-3675, 2018
强制性开放获取政策： US National Institutes of Health
Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data
V Chernozhukov, K Wuthrich, Y Zhu
Conference on Learning Theory (2018); arXiv preprint arXiv:1712.09089, 2018
强制性开放获取政策： US National Science Foundation
Constrained conditional moment restriction models
V Chernozhukov, WK Newey, A Santos
Econometrica 2023; arXiv preprint arXiv:1509.06311, 2023
强制性开放获取政策： US National Science Foundation
DoubleML-an object-oriented implementation of double machine learning in python
P Bach, V Chernozhukov, MS Kurz, M Spindler
Journal of Machine Learning Research 23 (53), 1-6, 2022
强制性开放获取政策： German Research Foundation
Empirical and multiplier bootstraps for suprema of empirical processes of increasing complexity, and related Gaussian couplings
V Chernozhukov, D Chetverikov, K Kato
Stochastic Processes and their Applications 126 (12), 3632-3651, 2016
强制性开放获取政策： US National Science Foundation
A t-test for synthetic controls
V Chernozhukov, K Wuthrich, Y Zhu
arXiv preprint arXiv:1812.10820, 2018
强制性开放获取政策： US National Science Foundation
LASSO-driven inference in time and space
V Chernozhukov, W Karl Härdle, C Huang, W Wang
The Annals of Statistics 49 (3), 1702-1735, 2021
强制性开放获取政策： German Research Foundation
Implementing intersection bounds in Stata
V Chernozhukov, W Kim, S Lee, A Rosen
Stata Journal 15 (1), 21-44, 2014
强制性开放获取政策： UK Economic and Social Research Council, European Commission
Nearly optimal central limit theorem and bootstrap approximations in high dimensions
V Chernozhukov, D Chetverikov, Y Koike
Annals of Applied Probability 2023; arXiv preprint 2020; arXiv:2012.09513, 2023
强制性开放获取政策： Japan Science and Technology Agency
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