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Featured Co-authors

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∙ 05/22/2023

A two-way heterogeneity model for dynamic networks

Analysis of networks that evolve dynamically requires the joint modellin...

0 Binyan Jiang, et al. ∙

∙ 02/15/2023

Optimal Subsampling Bootstrap for Massive Data

The bootstrap is a widely used procedure for statistical inference becau...

0 Yingying Ma, et al. ∙

∙ 12/13/2022

Testing the Graph of a Gaussian Graphical Model

The Gaussian graphical model is routinely employed to model the joint di...

0 Thien-Minh Le, et al. ∙

∙ 04/25/2022

Impact of Geographic Diversity on Citation of Collaborative Research

Diversity in human capital is widely seen as critical to creating holist...

0 Cian Naik, et al. ∙

∙ 12/14/2021

Linear Discriminant Analysis with High-dimensional Mixed Variables

Datasets containing both categorical and continuous variables are freque...

2 Binyan Jiang, et al. ∙

∙ 09/13/2021

Regression Analysis of Correlations for Correlated Data

Correlated data are ubiquitous in today's data-driven society. A fundame...

0 Jie Hu, et al. ∙

∙ 08/21/2021

A Sparse Random Graph Model for Sparse Directed Networks

An increasingly urgent task in analysis of networks is to develop statis...

0 Stefan Stein, et al. ∙

∙ 10/26/2020

A Sparse β-Model with Covariates for Networks

Data in the form of networks are increasingly encountered in modern scie...

0 Stefan Stein, et al. ∙

∙ 08/08/2019

Analysis of Networks via the Sparse β-Model

Data in the form of networks are increasingly available in a variety of ...

0 Mingli Chen, et al. ∙

∙ 10/26/2018

Spectral Analysis of High-dimensional Time Series

A useful approach for analysing multiple time series is via characterisi...

0 Mark Fiecas, et al. ∙

∙ 02/08/2016

DECOrrelated feature space partitioning for distributed sparse regression

Fitting statistical models is computationally challenging when the sampl...

0 Xiangyu Wang, et al. ∙

∙ 10/01/2015

QUDA: A Direct Approach for Sparse Quadratic Discriminant Analysis

Quadratic discriminant analysis (QDA) is a standard tool for classificat...

0 Binyan Jiang, et al. ∙

∙ 06/07/2015

No penalty no tears: Least squares in high-dimensional linear models

Ordinary least squares (OLS) is the default method for fitting linear mo...

0 Xiangyu Wang, et al. ∙

∙ 06/05/2015

High-dimensional Ordinary Least-squares Projection for Screening Variables

Variable selection is a challenging issue in statistical applications wh...

0 Xiangyu Wang, et al. ∙

∙ 02/24/2015

On the consistency theory of high dimensional variable screening

Variable screening is a fast dimension reduction technique for assisting...

0 Xiangyu Wang, et al. ∙

∙ 09/02/2011

Gradient-based kernel dimension reduction for supervised learning

This paper proposes a novel kernel approach to linear dimension reductio...

0 Kenji Fukumizu, et al. ∙

∙ 11/22/2010

Variational approximation for heteroscedastic linear models and matching pursuit algorithms

Modern statistical applications involving large data sets have focused a...

0 David J. Nott, et al. ∙