b'Jelena Bradic'

DeepAI

Featured Co-authors

Yi Yu
93 publications
Jianqing Fan
74 publications
Mladen Kolar
60 publications
Victor Chernozhukov
53 publications
Stefan Wager
48 publications
Richard J. Samworth
28 publications
Jing Zhou
21 publications
Yinchu Zhu
21 publications
Yuqian Zhang
21 publications
Jiaqi Guo
12 publications
Alexander Hanbo Li
11 publications

research

∙ 11/12/2021

Dynamic treatment effects: high-dimensional inference under model misspecification

This paper considers the inference for heterogeneous treatment effects i...

Yuqian Zhang, et al. ∙

research

∙ 10/10/2021

High-dimensional Inference for Dynamic Treatment Effects

This paper proposes a confidence interval construction for heterogeneous...

Jelena Bradic, et al. ∙

research

∙ 04/14/2021

Double Robust Semi-Supervised Inference for the Mean: Selection Bias under MAR Labeling with Decaying Overlap

Semi-supervised (SS) inference has received much attention in recent yea...

Yuqian Zhang, et al. ∙

research

∙ 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...

Jelena Bradic, et al. ∙

research

∙ 03/01/2021

Dynamic covariate balancing: estimating treatment effects over time

This paper discusses the problem of estimation and inference on time-var...

Davide Viviano, et al. ∙

research

∙ 02/01/2021

Learning to Combat Noisy Labels via Classification Margins

A deep neural network trained on noisy labels is known to quickly lose i...

Jason Z. Lin, et al. ∙

research

∙ 07/26/2020

DeepHazard: neural network for time-varying risks

Prognostic models in survival analysis are aimed at understanding the re...

Denise Rava, et al. ∙

research

∙ 06/12/2020

Detangling robustness in high dimensions: composite versus model-averaged estimation

Robust methods, though ubiquitous in practice, are yet to be fully under...

Jing Zhou, et al. ∙

research

∙ 05/25/2020

Fair Policy Targeting

One of the major concerns of targeting interventions on individuals in s...

Davide Viviano, et al. ∙

research

∙ 01/08/2020

Censored Quantile Regression Forest

Random forests are powerful non-parametric regression method but are sev...

Alexander Hanbo Li, et al. ∙

research

∙ 12/27/2019

Minimax Semiparametric Learning With Approximate Sparsity

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

Jelena Bradic, et al. ∙

research

∙ 06/29/2019

Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates

Estimating causal effects for survival outcomes in the high-dimensional ...

Jue Hou, et al. ∙

research

∙ 05/02/2019

Sparsity Double Robust Inference of Average Treatment Effects

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

Jelena Bradic, et al. ∙

research

∙ 04/02/2019

Synthetic learner: model-free inference on treatments over time

Understanding of the effect of a particular treatment or a policy pertai...

Davide Viviano, et al. ∙

research

∙ 02/08/2019

Censored Quantile Regression Forests

Random forests are powerful non-parametric regression method but are sev...

Alexander Hanbo Li, et al. ∙

research

∙ 02/02/2019

High-dimensional semi-supervised learning: in search for optimal inference of the mean

We provide a high-dimensional semi-supervised inference framework focuse...

Yuqian Zhang, et al. ∙

research

∙ 03/03/2018

Confidence intervals for high-dimensional Cox models

The purpose of this paper is to construct confidence intervals for the r...

Yi Yu, et al. ∙

research

∙ 02/26/2018

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

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

Jelena Bradic, et al. ∙

research

∙ 08/14/2017

Fixed effects testing in high-dimensional linear mixed models

Many scientific and engineering challenges -- ranging from pharmacokinet...

Jelena Bradic, et al. ∙

research

∙ 08/01/2017

Breaking the curse of dimensionality in regression

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

Yinchu Zhu, et al. ∙

research

∙ 07/29/2017

Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference

The purpose of this paper is to construct confidence intervals for the r...

Jue Hou, et al. ∙

research

∙ 05/06/2017

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

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

Jelena Bradic, et al. ∙

research

∙ 05/02/2017

A projection pursuit framework for testing general high-dimensional hypothesis

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

Yinchu Zhu, et al. ∙

research

∙ 02/20/2017

Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores

Hypothesis tests in models whose dimension far exceeds the sample size c...

Jelena Bradic, et al. ∙

research

∙ 10/14/2016

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

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

Yinchu Zhu, et al. ∙

research

∙ 10/10/2016

Linear Hypothesis Testing in Dense High-Dimensional Linear Models

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

Yinchu Zhu, et al. ∙

research

∙ 10/07/2016

Significance testing in non-sparse high-dimensional linear models

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

Yinchu Zhu, et al. ∙

research

∙ 09/22/2016

Robust Confidence Intervals in High-Dimensional Left-Censored Regression

This paper develops robust confidence intervals in high-dimensional and ...

Jelena Bradic, et al. ∙

research

∙ 10/05/2015

Boosting in the presence of outliers: adaptive classification with non-convex loss functions

This paper examines the role and efficiency of the non-convex loss funct...

Alexander Hanbo Li, et al. ∙

research

∙ 07/31/2015

Robustness in sparse linear models: relative efficiency based on robust approximate message passing

Understanding efficiency in high dimensional linear models is a longstan...

Jelena Bradic, et al. ∙