60 citations · 172 across the 17 of their papers we have counts for
19 papers
Understanding Best Subset Selection: A Tale of Two C(omplex)ities
Saptarshi Roy, Ambuj Tewari, Ziwei Zhu
We consider the problem of best subset selection (BSS) under high-dimensional sparse linear regression model. Recently, Guo et al. (2020) showed that the model selection performanc…
ReBoot: Distributed statistical learning via refitting bootstrap samples
Yumeng Wang, Ziwei Zhu, Xuming He
In this paper, we propose a one-shot distributed learning algorithm via refitting bootstrap samples, which we refer to as ReBoot. ReBoot refits a new model to mini-batches of boots…
Supervised Homogeneity Fusion: a Combinatorial Approach
Wen Wang, Shihao Wu, Ziwei Zhu +2
Fusing regression coefficients into homogenous groups can unveil those coefficients that share a common value within each group. Such groupwise homogeneity reduces the intrinsic di…
High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective
Saptarshi Roy, Ambuj Tewari, Ziwei Zhu
We study the problem of exact support recovery for high-dimensional sparse linear regression under independent Gaussian design when the signals are weak, rare, and possibly heterog…
High-dimensional inference via hybrid orthogonalization
Yang Li, Zemin Zheng, Jia Zhou +1
The past decade has witnessed a surge of endeavors in statistical inference for high-dimensional sparse regression, particularly via de-biasing or relaxed orthogonalization. Nevert…
Rank-Constrained Least-Squares: Prediction and Inference
Michael Law, Ya'acov Ritov, Ruixiang Zhang +1
In this work, we focus on the high-dimensional trace regression model with a low-rank coefficient matrix. We establish a nearly optimal in-sample prediction risk bound for the rank…
Volatility prediction comparison via robust volatility proxies: An empirical deviation perspective
Weichen Wang, Ran An, Ziwei Zhu
Volatility forecasting is crucial to risk management and portfolio construction. One particular challenge of assessing volatility forecasts is how to construct a robust proxy for t…
On sure early selection of the best subset
Ziwei Zhu, Shihao Wu
The early solution path, which tracks the first few variables that enter the model of a selection procedure, is of profound importance to scientific discoveries. In practice, it is…
Best subset selection is robust against design dependence
Yongyi Guo, Ziwei Zhu, Jianqing Fan
Best subset selection (BSS) is widely known as the holy grail for high-dimensional variable selection. Nevertheless, the notorious NP-hardness of BSS substantially restricts its pr…
Learning Markov models via low-rank optimization
Ziwei Zhu, Xudong Li, Mengdi Wang +1
Modeling unknown systems from data is a precursor of system optimization and sequential decision making. In this paper, we focus on learning a Markov model from a single trajectory…
High-dimensional principal component analysis with heterogeneous missingness
Ziwei Zhu, Tengyao Wang, Richard J. Samworth
We study the problem of high-dimensional Principal Component Analysis (PCA) with missing observations. In simple, homogeneous missingness settings with a noise level of constant or…
Robust high dimensional factor models with applications to statistical machine learning
Jianqing Fan, Kaizheng Wang, Yiqiao Zhong +1
Factor models are a class of powerful statistical models that have been widely used to deal with dependent measurements that arise frequently from various applications from genomic…
Principal component analysis for big data
Jianqing Fan, Qiang Sun, Wen-Xin Zhou +1
Big data is transforming our world, revolutionizing operations and analytics everywhere, from financial engineering to biomedical sciences. The complexity of big data often makes d…
Generalized High-Dimensional Trace Regression via Nuclear Norm Regularization
Jianqing Fan, Wenyan Gong, Ziwei Zhu
We study the generalized trace regression with a near low-rank regression coefficient matrix, which extends notion of sparsity for regression coefficient vectors. Specifically, giv…
Taming heavy-tailed features by shrinkage
Ziwei Zhu, Wenjing Zhou
In this work, we focus on a variant of the generalized linear model (GLM) called corrupted GLM (CGLM) with heavy-tailed features and responses. To robustify the statistical inferen…
Distributed Estimation of Principal Eigenspaces
Jianqing Fan, Dong Wang, Kaizheng Wang +1
Principal component analysis (PCA) is fundamental to statistical machine learning. It extracts latent principal factors that contribute to the most variation of the data. When data…
A Shrinkage Principle for Heavy-Tailed Data: High-Dimensional Robust Low-Rank Matrix Recovery
Jianqing Fan, Weichen Wang, Ziwei Zhu
This paper introduces a simple principle for robust high-dimensional statistical inference via an appropriate shrinkage on the data. This widens the scope of high-dimensional techn…
Heterogeneity Adjustment with Applications to Graphical Model Inference
Jianqing Fan, Han Liu, Weichen Wang +1
Heterogeneity is an unwanted variation when analyzing aggregated datasets from multiple sources. Though different methods have been proposed for heterogeneity adjustment, no system…
Distributed Estimation and Inference with Statistical Guarantees
Heather Battey, Jianqing Fan, Han Liu +2
This paper studies hypothesis testing and parameter estimation in the context of the divide and conquer algorithm. In a unified likelihood based framework, we propose new test stat…