b'Jelena Bradic'

research

∙ 05/22/2023

Semi-Supervised Causal Inference: Generalizable and Double Robust Inference for Average Treatment Effects under Selection Bias with Decaying Overlap

Average treatment effect (ATE) estimation is an essential problem in the...

0 Yuqian Zhang, et al. ∙

research

∙ 11/12/2021

Dynamic treatment effects: high-dimensional inference under model misspecification

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

0 Yuqian Zhang, et al. ∙

research

∙ 10/10/2021

High-dimensional Inference for Dynamic Treatment Effects

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

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 Jing Zhou, et al. ∙

research

∙ 05/25/2020

Fair Policy Targeting

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

0 Davide Viviano, et al. ∙

research

∙ 01/08/2020

Censored Quantile Regression Forest

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

1 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...

19 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 ...

0 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...

0 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...

0 Davide Viviano, et al. ∙

research

∙ 02/08/2019

Censored Quantile Regression Forests

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

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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...

0 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 ...

0 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 ...

0 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...

0 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...

0 Jelena Bradic, et al. ∙

Jelena Bradic

Featured Co-authors