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09/15/2023
A Random Graph-based Autoregressive Model for Networked Time Series
Contemporary time series data often feature objects connected by a socia...
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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...
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02/15/2023
Optimal Subsampling Bootstrap for Massive Data
The bootstrap is a widely used procedure for statistical inference becau...
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12/13/2022
Testing the Graph of a Gaussian Graphical Model
The Gaussian graphical model is routinely employed to model the joint di...
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04/25/2022
Impact of Geographic Diversity on Citation of Collaborative Research
Diversity in human capital is widely seen as critical to creating holist...
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12/14/2021
Linear Discriminant Analysis with High-dimensional Mixed Variables
Datasets containing both categorical and continuous variables are freque...
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09/13/2021
Regression Analysis of Correlations for Correlated Data
Correlated data are ubiquitous in today's data-driven society. A fundame...
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08/21/2021
A Sparse Random Graph Model for Sparse Directed Networks
An increasingly urgent task in analysis of networks is to develop statis...
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10/26/2020
A Sparse β-Model with Covariates for Networks
Data in the form of networks are increasingly encountered in modern scie...
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08/08/2019
Analysis of Networks via the Sparse β-Model
Data in the form of networks are increasingly available in a variety of ...
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10/26/2018
Spectral Analysis of High-dimensional Time Series
A useful approach for analysing multiple time series is via characterisi...
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02/08/2016
DECOrrelated feature space partitioning for distributed sparse regression
Fitting statistical models is computationally challenging when the sampl...
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10/01/2015
QUDA: A Direct Approach for Sparse Quadratic Discriminant Analysis
Quadratic discriminant analysis (QDA) is a standard tool for classificat...
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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...
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06/05/2015
High-dimensional Ordinary Least-squares Projection for Screening Variables
Variable selection is a challenging issue in statistical applications wh...
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02/24/2015
On the consistency theory of high dimensional variable screening
Variable screening is a fast dimension reduction technique for assisting...
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09/02/2011
Gradient-based kernel dimension reduction for supervised learning
This paper proposes a novel kernel approach to linear dimension reductio...
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11/22/2010
