activity
20162022
most citedPrincipal component analysis for big data

18 citations · 23 across the 4 of their papers we have counts for

collaborators

8 papers

stat.ML2022

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…

math.ST2021

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…

stat.ME2019

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…

stat.ME2019

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…

stat.ME2018

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…

stat.ME201818 cited

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…