This paper addresses the task of estimating a covariance matrix under a patternless sparsity
assumption. In contrast to existing approaches based on thresholding or shrinkage …

In the tensor data analysis, the Kronecker covariance structure plays a vital role in
unsupervised learning and regression. Under the Kronecker covariance model assumption …

A new strategy for probabilistic graphical modeling is developed that draws parallels to
community detection analysis. The method jointly estimates an undirected graph and …

In this letter we present a framework for estimating sparse covariance matrices, wherein we
solve the norm penalized maximum likelihood estimation problem using the extended …

Graphical models have exhibited their performance in numerous tasks ranging from
biological analysis to recommender systems. However, graphical models with hub nodes …

We propose a penalized likelihood method to fit the bivariate categorical response
regression model. Our method allows practitioners to estimate which predictors are …

Transcriptome-wide association studies based on genetically predicted gene expression
have the potential to identify novel regions associated with various complex traits. It has …

A new method is developed for performing sufficient dimension reduction when probabilistic
graphical models are being used to estimate parameters. The procedure enriches the …

In contemporary scientific studies, the data collected for classification becomes increasingly
complex as technology advances. In my dissertation, we mainly tackle the two following …

A large number of tensor datasets have been appearing in modern scientific research,
attracting much attention to the analysis of such datasets. Tensor data often have high …