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Matrix Graphical Model Via Joint Estimation of Partial Correlations
Hyewon Kim, Seongoh Park
Matrix graphical models aim to characterize conditional dependence structures in matrix-variate data under a separable covariance assumption. In this framework, the precision matri…
Graph Estimation Based on Neighborhood Selection for Matrix-variate Data
Minsub Shin, Johan Lim, Seongoh Park
Undirected graphical models are powerful tools for uncovering complex relationships among high-dimensional variables. This paper aims to fully recover the structure of an undirecte…
Linear Shrinkage Convexification of Penalized Linear Regression With Missing Data
Seongoh Park, Seongjin Lee, Nguyen Thi Hai Yen +2
One of the common challenges faced by researchers in recent data analysis is missing values. In the context of penalized linear regression, which has been extensively explored over…
Estimating High-dimensional Covariance and Precision Matrices under General Missing Dependence
Seongoh Park, Xinlei Wang, Johan Lim
A sample covariance matrix of completely observed data is the key statistic in a large variety of multivariate statistical procedures, such as structured covarianc…
Testing for Stochastic Order in Interval-Valued Data
Hyejeong Choi, Johan Lim, Minjung Kwak +1
We construct a procedure to test the stochastic order of two samples of interval-valued data. We propose a test statistic which belongs to U-statistic and derive its asymptotic dis…