no code implementations • 17 Feb 2024 • Yuqian Zhang, Weijie Ji, Jelena Bradic
While random forests are commonly used for regression problems, existing methods often lack adaptability in complex situations or lose optimality under simple, smooth scenarios.
no code implementations • 22 May 2023 • Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic
Notably, we relax the need for a positivity condition, commonly required in the missing data literature, and allow uniform decay of labeling propensity scores with sample size, accommodating faster growth of unlabeled data.
no code implementations • 12 Nov 2021 • Yuqian Zhang, Weijie Ji, Jelena Bradic
This paper introduces a new approach by proposing novel, robust estimators for both treatment assignments and outcome models.
no code implementations • 10 Oct 2021 • Jelena Bradic, Weijie Ji, Yuqian Zhang
Estimating dynamic treatment effects is a crucial endeavor in causal inference, particularly when confronted with high-dimensional confounders.
1 code implementation • 14 Apr 2021 • Yuqian Zhang, Abhishek Chakrabortty, Jelena Bradic
Apart from a moderate-sized labeled data, L, the SS setting is characterized by an additional, much larger sized, unlabeled data, U.
no code implementations • 21 Mar 2021 • Jelena Bradic, Yinchu Zhu
Breiman challenged statisticians to think more broadly, to step into the unknown, model-free learning world, with him paving the way forward.
no code implementations • 1 Mar 2021 • Davide Viviano, Jelena Bradic
We propose a method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on treatment trajectories; (iii) heterogeneity of treatment effects.
no code implementations • 1 Feb 2021 • Jason Z. Lin, Jelena Bradic
A deep neural network trained on noisy labels is known to quickly lose its power to discriminate clean instances from noisy ones.
1 code implementation • 26 Jul 2020 • Denise Rava, Jelena Bradic
Our approach is tailored for a wide range of continuous hazards forms, with the only restriction of being additive in time.
no code implementations • 12 Jun 2020 • Jing Zhou, Gerda Claeskens, Jelena Bradic
We find, however, that model-averaged and composite quantile estimators often outperform least-squares methods, even in the case of Gaussian model noise.
no code implementations • 25 May 2020 • Davide Viviano, Jelena Bradic
One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities across sensitive attributes such as age, gender, or race.
no code implementations • 8 Jan 2020 • Alexander Hanbo Li, Jelena Bradic
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases.
no code implementations • 27 Dec 2019 • Jelena Bradic, Victor Chernozhukov, Whitney K. Newey, Yinchu Zhu
This paper is about the feasibility and means of root-n consistently estimating linear, mean-square continuous functionals of a high dimensional, approximately sparse regression.
no code implementations • 29 Jun 2019 • Jue Hou, Jelena Bradic, Ronghui Xu
Estimating causal effects for survival outcomes in the high-dimensional setting is an extremely important topic for many biomedical applications as well as areas of social sciences.
no code implementations • 2 May 2019 • Jelena Bradic, Stefan Wager, Yinchu Zhu
Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice.
Statistics Theory Econometrics Methodology Statistics Theory
no code implementations • 2 Apr 2019 • Davide Viviano, Jelena Bradic
Understanding the effect of a particular treatment or a policy pertains to many areas of interest, ranging from political economics, marketing to healthcare.
1 code implementation • 8 Feb 2019 • Alexander Hanbo Li, Jelena Bradic
Random forests are powerful non-parametric regression method but are severely limited in their usage in the presence of randomly censored observations, and naively applied can exhibit poor predictive performance due to the incurred biases.
no code implementations • 2 Feb 2019 • Yuqian Zhang, Jelena Bradic
We provide a high-dimensional semi-supervised inference framework focused on the mean and variance of the response.
no code implementations • 26 Feb 2018 • Jelena Bradic, Jianqing Fan, Yinchu Zhu
Uniform non-testability identifies a collection of alternatives such that the power of any test, against any alternative in the group, is asymptotically at most equal to the nominal size.
no code implementations • 14 Aug 2017 • Jelena Bradic, Gerda Claeskens, Thomas Gueuning
A robust matching moment construction is used for creating a test that adapts to the size of the model sparsity.
no code implementations • 1 Aug 2017 • Yinchu Zhu, Jelena Bradic
In this article, we are interested in conducting large-scale inference in models that might have signals of mixed strengths.
no code implementations • 29 Jul 2017 • Jue Hou, Jelena Bradic, Ronghui Xu
The purpose of this paper is to construct confidence intervals for the regression coefficients in the Fine-Gray model for competing risks data with random censoring, where the number of covariates can be larger than the sample size.
no code implementations • 6 May 2017 • Jelena Bradic, Yinchu Zhu
We provide comments on the article "High-dimensional simultaneous inference with the bootstrap" by Ruben Dezeure, Peter Buhlmann and Cun-Hui Zhang.
no code implementations • 2 May 2017 • Yinchu Zhu, Jelena Bradic
We propose a new inference method developed around the hypothesis adaptive projection pursuit framework, which solves the testing problems in the most general case.
no code implementations • 20 Feb 2017 • Jelena Bradic, Mladen Kolar
The main technical result are the development of a Bahadur representation of the debiasing estimator that is uniform over a range of quantiles and uniform convergence of the quantile process to the Brownian bridge process, which are of independent interest.
no code implementations • 14 Oct 2016 • Yinchu Zhu, Jelena Bradic
In analyzing high-dimensional models, sparsity of the model parameter is a common but often undesirable assumption.
no code implementations • 10 Oct 2016 • Yinchu Zhu, Jelena Bradic
The test statistics are constructed in such a way that lack of sparsity in the original model parameter does not present a problem for the theoretical justification of our procedures.
no code implementations • 7 Oct 2016 • Yinchu Zhu, Jelena Bradic
We show that existing inferential methods are sensitive to the sparsity assumption, and may, in turn, result in the severe lack of control of Type-I error.
no code implementations • 22 Sep 2016 • Jelena Bradic, Jiaqi Guo
In this paper, we develop smoothed estimating equations that augment the de-biasing method, such that the resulting estimator is adaptive to censoring and is more robust to the misspecification of the error distribution.
no code implementations • 5 Oct 2015 • Alexander Hanbo Li, Jelena Bradic
Along with the Arch Boosting framework, the non-convex losses lead to the new class of boosting algorithms, named adaptive, robust, boosting (ARB).
no code implementations • 31 Jul 2015 • Jelena Bradic
We observe this pattern for all choices of the number of non-zero parameters $s$, both $s \leq n$ and $s \approx n$.
no code implementations • 14 Jun 2013 • Jelena Bradic
The proposed method is based on careful combination of penalized estimators, each applied to a random projection of the sample space into a low-dimensional space.
no code implementations • 18 Jul 2012 • Jelena Bradic, Rui Song
To better understand the interplay of censoring and sparsity we develop finite sample properties of nonparametric Cox proportional hazard's model.
no code implementations • 25 Oct 2010 • Jelena Bradic, Jianqing Fan, Jiancheng Jiang
High throughput genetic sequencing arrays with thousands of measurements per sample and a great amount of related censored clinical data have increased demanding need for better measurement specific model selection.