Yinchu Zhu | DeepAI

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Featured Co-authors

Jianqing Fan
83 publications
Victor Chernozhukov
54 publications
Stefan Wager
51 publications
Christian Hansen
32 publications
Jelena Bradic
31 publications
Yuan Liao
16 publications
Whitney K. Newey
10 publications
Kaspar Wuthrich
6 publications
Allan Timmermann
1 publication

∙ 06/21/2022

A sufficient and necessary condition for identification of binary choice models with fixed effects

We study the identification of binary choice models with fixed effects. ...

0 Yinchu Zhu, et al. ∙

∙ 07/06/2021

Inference for Low-Rank Models

This paper studies inference in linear models whose parameter of interes...

0 Victor Chernozhukov, et al. ∙

∙ 03/24/2021

Phase transition of the monotonicity assumption in learning local average treatment effects

We consider the setting in which a strong binary instrument is available...

0 Yinchu Zhu, et al. ∙

∙ 03/21/2021

Comments on Leo Breiman's paper 'Statistical Modeling: The Two Cultures' (Statistical Science, 2001, 16(3), 199-231)

Breiman challenged statisticians to think more broadly, to step into the...

0 Jelena Bradic, et al. ∙

∙ 06/05/2020

Can Two Forecasts Have the Same Conditional Expected Accuracy?

The method for testing equal predictive accuracy for pairs of forecastin...

0 Yinchu Zhu, et al. ∙

∙ 12/27/2019

Minimax Semiparametric Learning With Approximate Sparsity

Many objects of interest can be expressed as a linear, mean square conti...

19 Jelena Bradic, et al. ∙

∙ 10/23/2019

How well can we learn large factor models without assuming strong factors?

In this paper, we consider the problem of learning models with a latent ...

0 Yinchu Zhu, et al. ∙

∙ 09/17/2019

Distributional conformal prediction

We propose a robust method for constructing conditionally valid predicti...

0 Victor Chernozhukov, et al. ∙

∙ 05/02/2019

Sparsity Double Robust Inference of Average Treatment Effects

Many popular methods for building confidence intervals on causal effects...

0 Jelena Bradic, et al. ∙

∙ 12/19/2018

Inference For Heterogeneous Effects Using Low-Rank Estimations

We study a panel data model with general heterogeneous effects, where sl...

0 Victor Chernozhukov, et al. ∙

∙ 05/17/2018

k-step correction for mixed integer linear programming: a new approach for instrumental variable quantile regressions and related problems

This paper proposes a new framework for estimating instrumental variable...

0 Yinchu Zhu, et al. ∙

∙ 05/17/2018

Mixed integer linear programming: a new approach for instrumental variable quantile regressions and related problems

This paper proposes a new framework for estimating instrumental variable...

0 Yinchu Zhu, et al. ∙

∙ 02/26/2018

Testability of high-dimensional linear models with non-sparse structures

This paper studies hypothesis testing and confidence interval constructi...

0 Jelena Bradic, et al. ∙

∙ 02/17/2018

Exact and Robust Conformal Inference Methods for Predictive Machine Learning With Dependent Data

We extend conformal inference to general settings that allow for time se...

0 Victor Chernozhukov, et al. ∙

∙ 12/25/2017

An Exact and Robust Conformal Inference Method for Counterfactual and Synthetic Controls

This paper introduces new inference methods for counterfactual and synth...

0 Victor Chernozhukov, et al. ∙

∙ 08/01/2017

Breaking the curse of dimensionality in regression

Models with many signals, high-dimensional models, often impose structur...

0 Yinchu Zhu, et al. ∙

∙ 05/06/2017

Comments on `High-dimensional simultaneous inference with the bootstrap'

We provide comments on the article "High-dimensional simultaneous infere...

0 Jelena Bradic, et al. ∙

∙ 05/02/2017

A projection pursuit framework for testing general high-dimensional hypothesis

This article develops a framework for testing general hypothesis in high...

0 Yinchu Zhu, et al. ∙

∙ 10/14/2016

Two-sample testing in non-sparse high-dimensional linear models

In analyzing high-dimensional models, sparsity of the model parameter is...

0 Yinchu Zhu, et al. ∙

∙ 10/10/2016

Linear Hypothesis Testing in Dense High-Dimensional Linear Models

We propose a methodology for testing linear hypothesis in high-dimension...

0 Yinchu Zhu, et al. ∙

∙ 10/07/2016

Significance testing in non-sparse high-dimensional linear models

In high-dimensional linear models, the sparsity assumption is typically ...

0 Yinchu Zhu, et al. ∙

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