Yinchu Zhu
Yinchu Zhu
Assistant Professor of Economics, Brandeis University
Verifierad e-postadress på brandeis.edu - Startsida
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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
782021
Linear hypothesis testing in dense high-dimensional linear models
Y Zhu, J Bradic
Journal of the American Statistical Association 113 (524), 1583-1600, 2018
572018
Significance testing in non-sparse high-dimensional linear models
Y Zhu, J Bradic
Electronic Journal of Statistics 12 (2), 3312-3364, 2018
322018
Exact and robust conformal inference methods for predictive machine learning with dependent data
V Chernozhukov, K Wüthrich, Z Yinchu
Conference On Learning Theory, 732-749, 2018
222018
Practical and robust -test based inference for synthetic control and related methods
V Chernozhukov, K Wuthrich, Y Zhu
arXiv. org Papers, 2020
212020
Distributional conformal prediction
V Chernozhukov, K Wüthrich, Y Zhu
arXiv preprint arXiv:1909.07889, 2019
192019
Sparsity double robust inference of average treatment effects
J Bradic, S Wager, Y Zhu
arXiv preprint arXiv:1905.00744, 2019
192019
Quantile spacings: A simple method for the joint estimation of multiple quantiles without crossing
L Schmidt, Y Zhu
Available at SSRN 2220901, 2016
182016
Inference for heterogeneous effects using low-rank estimations
V Chernozhukov, CB Hansen, Y Liao, Y Zhu
CEMMAP working paper, 2019
142019
Testability of high-dimensional linear models with non-sparse structures
J Bradic, J Fan, Y Zhu
arXiv preprint arXiv:1802.09117, 2018
142018
Variable selection in panel models with breaks
SC Smith, A Timmermann, Y Zhu
Journal of econometrics 212 (1), 323-344, 2019
122019
A projection pursuit framework for testing general high-dimensional hypothesis
Y Zhu, J Bradic
arXiv preprint arXiv:1705.01024, 2017
112017
Breaking the curse of dimensionality in regression
Y Zhu, J Bradic
arXiv preprint arXiv:1708.00430, 2017
102017
Comparing forecasting performance with panel data
A Timmermann, Y Zhu
CEPR Discussion Paper No. DP13746, 2019
92019
Inference on average treatment effects in aggregate panel data settings
V Chernozhukov, K Wüthrich, Y Zhu
cemmap working paper, 2019
92019
Hypothesis testing in non-sparse high-dimensional linear models
Y Zhu, J Bradic
arXiv preprint arXiv:1610.02122, 2016
62016
Can Two Forecasts Have the Same Conditional Expected Accuracy?
Y Zhu, A Timmermann
arXiv preprint arXiv:2006.03238, 2020
42020
Monitoring forecasting performance
A Timmermann, Y Zhu
4*2017
Inference for low-rank models
V Chernozhukov, C Hansen, Y Liao, Y Zhu
arXiv preprint arXiv:2107.02602, 2021
32021
Minimax semiparametric learning with approximate sparsity
J Bradic, V Chernozhukov, WK Newey, Y Zhu
arXiv preprint arXiv:1912.12213, 2019
32019
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Artiklar 1–20