Area of research
Computational Mathematics · Statistics and Probability
Research interest
Research topics from publications: The fused Kolmogorov filter: A nonparametric model-free screening method; Covariate-Adjusted Tensor Classification in High Dimensions; An Iterative Penalized Least Squares Approach to Sparse Canonical Correlation Analysis; A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering; Multiclass Sparse Discriminant Analysis; Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction; Sparse semiparametric discriminant analysis; Fast and Separable Estimation in High-Dimensional Tensor Gaussian Graphical Models; Model-free envelope dimension selection; Efficient Integration of Sufficient Dimension Reduction and Prediction in Discriminant Analysis. Representative work: A new model-free screening method called the fused Kolmogorov filter is proposed for high-dimensional data analysis. This new method is fully nonparametric and can work with many types of covariates and response variables, including continuous, discrete and categorical variables. We apply the fused Kolmogorov filter to deal with variable screening problems emerging from a wide range of applications, such as multiclass classification, nonparametric regression and Poisson regression, among others. It is shown that the fused Kolmogorov filter enjoys the sure screening property under weak regularity conditions that are much milder than those required for many existing nonparametric screening met In contemporary scientific research, it is often of great interest to predict a categorical response based on a high-dimensional tensor (i.e., multi-dimensional array) and additional covariates. Motivated by applications in science and engineering, we propose a comprehensive and interpretable discriminant analysis model, called the CATCH model (short for covariate-adjusted tensor classification in high-dimensions). The CATCH model efficiently integrates the covariates and the tensor to predict the categorical outcome. It also jointly explains the complicated relationships among the covariates, the tensor predictor, and the categorical response. The tensor structure is used to achieve easy in
Leveraging independence in high-dimensional mixed linear regression
Tensor mixture discriminant analysis with applications to sensor array data analysis
Decorrelated nearest shrunken centroids for tensor data
Optimality in high-dimensional tensor discriminant analysis
Model-Based Tensor Low-Rank Clustering
Envelopes and principal component regression
The Tucker Low-Rank Classification Model for Tensor Data
Optimality in High-Dimensional Tensor Discriminant Analysis
Statistical Methods for Tensor Data Analysis
Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction
Coordinatewise Gaussianization: Theories and Applications
Bayesian Regression Analysis of Skewed Tensor Responses
The robust nearest shrunken centroids classifier for high-dimensional heavy-tailed data
A general framework for tensor screening through smoothing
A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering
Fast and Separable Estimation in High-Dimensional Tensor Gaussian Graphical Models
Model-based clustering with envelopes
A Doubly Enhanced EM Algorithm for Model-Based Tensor Clustering
An Iterative Penalized Least Squares Approach to Sparse Canonical Correlation Analysis
Efficient computation for differential network analysis with applications to quadratic discriminant analysis
Covariate-Adjusted Tensor Classification in High Dimensions
Model-free envelope dimension selection
Efficient Integration of Sufficient Dimension Reduction and Prediction in Discriminant Analysis
Covariate-Adjusted Tensor Classification in High-Dimensions
Multiclass Sparse Discriminant Analysis
The fused Kolmogorov filter: A nonparametric model-free screening method
Sparse semiparametric discriminant analysis
Nonparametric Variable Transformation in Sufficient Dimension Reduction