Classification of Higher-order Data with Separable Covariance and Structured Multiplicative or Additive Mean Models

DOI10.1080/03610926.2013.841931zbMath1462.62390OpenAlexW2017845967MaRDI QIDQ5419342

Anuradha Roy, Ricardo Leiva

Publication date: 6 June 2014

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.841931

zbMATH Keywords

maximum likelihood estimatesclassification ruleshigher-order data\(\kappa\)-separable covariance structurestructured additive mean modelstructured multiplicative mean model

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

A note on necessary and sufficient conditions of existence and uniqueness for the maximum likelihood estimator of a Kronecker-product variance-covariance matrix ⋮ Estimation of a multiplicative correlation structure in the large dimensional case ⋮ Supervised classifiers for high-dimensional higher-order data with locally doubly exchangeable covariance structure

Cites Work

• Sparse inverse covariance estimation with the graphical lasso
• Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure
• Discriminant analysis of multivariate repeated measures data with Kronecker product structured covariance matrices
• Linear discrimination for multi-level multivariate data with separable means and jointly equicorrelated covariance structure
• The multilinear normal distribution: introduction and some basic properties
• Models with a Kronecker product covariance structure: estimation and testing
• Linear discrimination with equicorrelated training vectors
• Simultaneous classification and relevant feature identification in high-dimensional spaces: Application to molecular profiling data
• Classification rules for triply multivariate data with an AR(1) correlation structure on the repeated measures over time
• Discrimination with jointly equicorrelated multi-level multivariate data
• Selecting variables for discrimination when covariance matrices are unequal
• Maximum likelihood estimation for the tensor normal distribution: Algorithm, minimum sample size, and empirical bias and dispersion
• On discrimination and classification with multivariate repeated measures data
• The likelihood ratio test for a separable covariance matrix
• Data augmentation for support vector machines
• More on the Kronecker Structured Covariance Matrix
• Covariance structure approximation via gLasso in high-dimensional supervised classification
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• The mle algorithm for the matrix normal distribution
• General class of covariance structures for two or more repeated factors in longitudinal data analysis
• Distances between normal populations when covariance matrices are unequal
• Applied Multiway Data Analysis

This page was built for publication: Classification of Higher-order Data with Separable Covariance and Structured Multiplicative or Additive Mean Models