Outliers widely occur in big-data applications and may severely affect statistical estimation
and inference. In this article, a framework of outlier-resistant estimation is introduced to …

While matrix-covariate regression models have been studied in many existing works,
classical statistical and computational methods for the analysis of the regression coefficient …

Big-data applications often involve a vast number of observations and features, creating new
challenges for variable selection and parameter estimation. This paper presents a novel …

Skewness is a common occurrence in statistical applications. In recent years, various
distribution families have been proposed to model skewed data by introducing unequal …

Tukey's depth offers a powerful tool for nonparametric inference and estimation, but also
encounters serious computational and methodological difficulties in modern statistical data …

While matrix variate regression models have been studied in many existing works, classical
statistical and computational methods for the analysis of the regression coefficient estimation …

Modern high‐dimensional methods often adopt the 'bet on sparsity'principle, while in
supervised multivariate learning statisticians may face 'dense'problems with a large number …

Tukey's depth offers a powerful tool for nonparametric inference and estimation but also
encounters serious computational and methodological difficulties in modern statistical data …