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07/05/2023
High-Dimensional Expected Shortfall Regression
The expected shortfall is defined as the average over the tail below (or...
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03/06/2023
Huber Principal Component Analysis for Large-dimensional Factor Models
Factor models have been widely used in economics and finance. However, t...
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01/01/2023
Iterative Least Squares Algorithm for Large-dimensional Matrix Factor Model by Random Projection
The matrix factor model has drawn growing attention for its advantage in...
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12/11/2022
Robust Estimation and Inference for Expected Shortfall Regression with Many Regressors
Expected Shortfall (ES), also known as superquantile or Conditional Valu...
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12/11/2022
Retire: Robust Expectile Regression in High Dimensions
High-dimensional data can often display heterogeneity due to heterosceda...
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10/23/2022
Scalable estimation and inference for censored quantile regression process
Censored quantile regression (CQR) has become a valuable tool to study t...
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08/21/2022
High-Dimensional Composite Quantile Regression: Optimal Statistical Guarantees and Fast Algorithms
The composite quantile regression (CQR) was introduced by Zou and Yuan [...
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07/20/2022
Matrix Kendall's tau in High-dimensions: A Robust Statistic for Matrix Factor Model
In this article, we first propose generalized row/column matrix Kendall'...
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05/05/2022
A Unified Algorithm for Penalized Convolution Smoothed Quantile Regression
Penalized quantile regression (QR) is widely used for studying the relat...
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03/20/2022
How do noise tails impact on deep ReLU networks?
This paper investigates the stability of deep ReLU neural networks for n...
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10/25/2021
Communication-Efficient Distributed Quantile Regression with Optimal Statistical Guarantees
We address the problem of how to achieve optimal inference in distribute...
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09/12/2021
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization
ℓ_1-penalized quantile regression is widely used for analyzing high-dime...
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06/30/2021
Adaptive Capped Least Squares
This paper proposes the capped least squares regression with an adaptive...
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12/09/2020
Smoothed Quantile Regression with Large-Scale Inference
Quantile regression is a powerful tool for learning the relationship bet...
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10/04/2019
On the Asymptotic Distribution of the Scan Statistic for Point Clouds
We derive the large-sample distribution of several variants of the scan ...
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07/09/2019
Nonconvex Regularized Robust Regression with Oracle Properties in Polynomial Time
This paper investigates tradeoffs among optimization errors, statistical...
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03/18/2019
Robust Inference via Multiplier Bootstrap
This paper investigates the theoretical underpinnings of two fundamental...
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11/05/2018
User-Friendly Covariance Estimation for Heavy-Tailed Distributions: A Survey and Recent Results
We offer a survey of selected recent results on covariance estimation fo...
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11/05/2018
User-Friendly Covariance Estimation for Heavy-Tailed Distributions
We propose user-friendly covariance matrix estimators that are robust ag...
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01/05/2018
Principal component analysis for big data
Big data is transforming our world, revolutionizing operations and analy...
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11/15/2017
FARM-Test: Factor-Adjusted Robust Multiple Testing with False Discovery Control
Large-scale multiple testing with correlated and heavy-tailed data arise...
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11/15/2017
A New Perspective on Robust M-Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing
Heavy-tailed errors impair the accuracy of the least squares estimate, w...
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09/24/2016
Max-Norm Optimization for Robust Matrix Recovery
This paper studies the matrix completion problem under arbitrary samplin...
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09/24/2013
A Max-Norm Constrained Minimization Approach to 1-Bit Matrix Completion
We consider in this paper the problem of noisy 1-bit matrix completion u...
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03/02/2013