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A note on the Bayesian analysis of experiments with correlated multiple responses using a matrix‐logarithmic covariance model
Author(s) -
Hamada Michael S.,
Jaramillo Brandon M.,
Chiao ChihHua
Publication year - 2019
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2540
Subject(s) - covariance matrix , covariance , rational quadratic covariance function , law of total covariance , estimation of covariance matrices , covariance intersection , logarithm , mathematics , matérn covariance function , covariance function , statistics , matrix (chemical analysis) , covariance mapping , mathematical analysis , materials science , composite material
Abstract In the literature, analysis of multiple responses from experiments with replicates has modeled the covariance matrix directly as linear models of the transformed variances and correlations, ie, covariance modeling. This article considers models based on the matrix‐logarithm of the covariance matrix. This so‐called log‐covariance modeling is illustrated with data from actual experiments and compared with the traditional covariance modeling.