Inverse prediction for multivariate mixed models with standard software

Lynn R. LaMotte () and Jeffrey D. Wells ()
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Lynn R. LaMotte: LSU Health Sciences Center
Jeffrey D. Wells: Florida International University

Statistical Papers, 2016, vol. 57, issue 4, No 5, 929-938

Abstract: Abstract Inverse prediction (IP) is reputed to be computationally inconvenient for multivariate responses. This paper describes how IP can be formulated in terms of a general linear mixed model, along with a flexible modeling approach for both mean vectors and variance–covariance matrices. It illustrates that results can be had as standard output from widely-available statistical computing packages.

Keywords: Heteroscedastic multivariate models; Multivariate calibration; Forensic entomology; 62H15; 62J05; 62H30 (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00362-016-0815-2

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