Robust factored principal component analysis for matrix-valued outlier accommodation and detection
From MaRDI portal
Publication:6111536
DOI10.1016/j.csda.2022.107657arXiv2112.06760OpenAlexW4200628924MaRDI QIDQ6111536
No author found.
Publication date: 7 July 2023
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2112.06760
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A robust factor analysis model using the restricted skew-\(t\) distribution
- Finite mixtures of matrix normal distributions for classifying three-way data
- Extension of the mixture of factor analyzers model to incorporate the multivariate \(t\)-distribution
- Mixtures of common \(t\)-factor analyzers for modeling high-dimensional data with missing values
- Principal component analysis.
- Separable linear discriminant analysis
- Robust clustering of multiply censored data via mixtures of \(t\) factor analyzers
- Model-based clustering of censored data via mixtures of factor analyzers
- Capturing patterns via parsimonious \(t\) mixture models
- Elliptically Contoured Models in Statistics and Portfolio Theory
- Estimation in multivariate t linear mixed models for multiple longitudinal data
- Parameter expansion to accelerate EM: the PX-EM algorithm
- The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
- Classification With the Matrix-Variate-t Distribution
- On the Breakdown Properties of Some Multivariate M-Functionals*
- Generalized low rank approximations of matrices
This page was built for publication: Robust factored principal component analysis for matrix-valued outlier accommodation and detection