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Inverse prediction for multivariate mixed models with standard software

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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.

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Acknowledgments

This project was supported by Award No. 2013-DN-BX-K042, awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect those of the Department of Justice.

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Authors and Affiliations

  1. LSU Health Sciences Center, New Orleans, LA, USA

    Lynn R. LaMotte

  2. Florida International University, Miami, FL, USA

    Jeffrey D. Wells

Authors
  1. Lynn R. LaMotte
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  2. Jeffrey D. Wells
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Correspondence to Lynn R. LaMotte.

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LaMotte, L.R., Wells, J.D. Inverse prediction for multivariate mixed models with standard software. Stat Papers 57, 929–938 (2016). https://doi.org/10.1007/s00362-016-0815-2

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  • DOI: https://doi.org/10.1007/s00362-016-0815-2

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