Classification rules for triply multivariate data with an AR(1) correlation structure on the repeated measures over time

DOI10.1016/j.jspi.2008.11.013zbMath1162.62059OpenAlexW1994219437MaRDI QIDQ1021993

Ricardo Leiva, Anuradha Roy

Publication date: 9 June 2009

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2008.11.013

zbMATH Keywords

maximum likelihood estimatesautoregressive covariance structureclassification ruleKronecker product covariance matrixmultivariate equicorrelationstructure on meantriply multivariate data

Mathematics Subject Classification ID

Related Items (7)

Best unbiased estimates for parameters of three-level multivariate data with doubly exchangeable covariance structure ⋮ Linear discrimination for multi-level multivariate data with separable means and jointly equicorrelated covariance structure ⋮ Supervised classifiers for high-dimensional higher-order data with locally doubly exchangeable covariance structure ⋮ Linear discrimination for three-level multivariate data with a separable additive mean vector and a doubly exchangeable covariance structure ⋮ Likelihood ratio tests for triply multivariate data with structured correlation on spatial repeated measurements ⋮ Estimating and Testing a Structured Covariance Matrix for Three-Level Multivariate Data ⋮ Classification of Higher-order Data with Separable Covariance and Structured Multiplicative or Additive Mean Models

Cites Work

• Linear discrimination with equicorrelated training vectors
• Likelihood ratio tests for triply multivariate data with structured correlation on spatial repeated measurements
• Discrimination with jointly equicorrelated multi-level multivariate data
• On discrimination and classification with multivariate repeated measures data
• Some Theorems on Matrix Differentiation with Special Reference to Kronecker Matrix Products
• An Inverse Matrix Adjustment Arising in Discriminant Analysis
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