60 citations · 209 across the 14 of their papers we have counts for
14 papers
A review of distributed statistical inference
Yuan Gao, Weidong Liu, Hansheng Wang +3
The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to deve…
Subsampling and Jackknifing: A Practically Convenient Solution for Large Data Analysis with Limited Computational Resources
Shuyuan Wu, Xuening Zhu, Hansheng Wang
Modern statistical analysis often encounters datasets with large sizes. For these datasets, conventional estimation methods can hardly be used immediately because practitioners oft…
Network Gradient Descent Algorithm for Decentralized Federated Learning
Shuyuan Wu, Danyang Huang, Hansheng Wang
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it…
An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models
Yuan Gao, Xuening Zhu, Haobo Qi +3
Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descen…
A Sequential Addressing Subsampling Method for Massive Data Analysis under Memory Constraint
Rui Pan, Yingqiu Zhu, Baishan Guo +2
The emergence of massive data in recent years brings challenges to automatic statistical inference. This is particularly true if the data are too numerous to be read into memory as…
On the Subbagging Estimation for Massive Data
Tao Zou, Xian Li, Xuan Liang +1
This article introduces subbagging (subsample aggregating) estimation approaches for big data analysis with memory constraints of computers. Specifically, for the whole dataset wit…
Estimating Extreme Value Index by Subsampling for Massive Datasets with Heavy-Tailed Distributions
Yongxin Li, Liujun Chen, Deyuan Li +1
Modern statistical analyses often encounter datasets with massive sizes and heavy-tailed distributions. For datasets with massive sizes, traditional estimation methods can hardly b…
Hyperparameter Selection for Subsampling Bootstraps
Yingying Ma, Hansheng Wang
Massive data analysis becomes increasingly prevalent, subsampling methods like BLB (Bag of Little Bootstraps) serves as powerful tools for assessing the quality of estimators for m…
Efficient Estimation for Generalized Linear Models on a Distributed System with Nonrandomly Distributed Data
Feifei Wang, Danyang Huang, Yingqiu Zhu +1
Distributed systems have been widely used in practice to accomplish data analysis tasks of huge scales. In this work, we target on the estimation problem of generalized linear mode…
Least Squares Approximation for a Distributed System
Xuening Zhu, Feng Li, Hansheng Wang
In this work, we develop a distributed least squares approximation (DLSA) method that is able to solve a large family of regression problems (e.g., linear regression, logistic regr…
Learning Human Activity Patterns using Clustered Point Processes with Active and Inactive States
Jingfei Zhang, Biao Cai, Xuening Zhu +3
Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and…
Banded Spatio-Temporal Autoregressions
Zhaoxing Gao, Yingying Ma, Hansheng Wang +1
We propose a new class of spatio-temporal models with unknown and banded autoregressive coefficient matrices. The setting represents a sparse structure for high-dimensional spatial…
High-dimensional influence measure
Junlong Zhao, Chenlei Leng, Lexin Li +1
Influence diagnosis is important since presence of influential observations could lead to distorted analysis and misleading interpretations. For high-dimensional data, it is partic…
Contour projected dimension reduction
Ronghua Luo, Hansheng Wang, Chih-Ling Tsai
In regression analysis, we employ contour projection (CP) to develop a new dimension reduction theory. Accordingly, we introduce the notions of the central contour subspace and gen…