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stat.ME2025
Highly robust factored principal component analysis for matrix-valued outlier accommodation and explainable detection via matrix minimum covariance determinant
Wenhui Wu, Changchun Shang, Jianhua Zhao +2
Principal component analysis (PCA) is a classical and widely used method for dimensionality reduction, with applications in data compression, computer vision, pattern recognition,…
stat.ME2025
Matrix Healy Plot: A Practical Tool for Visual Assessment of Matrix-Variate Normality
Fen Jiang, Jianhua Zhao, Changchun Shang +3
Matrix-valued data, where each observation is represented as a matrix, frequently arises in various scientific disciplines. Modeling such data often relies on matrix-variate normal…
stat.ME2024
A novel robust meta-analysis model using the distribution for outlier accommodation and detection
Yue Wang, Jianhua Zhao, Fen Jiang +2
Random effects meta-analysis model is an important tool for integrating results from multiple independent studies. However, the standard model is based on the assumption of normal…