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Inverse prediction for multivariate mixed models with standard software

Author

Listed:
  • Lynn R. LaMotte

    (LSU Health Sciences Center)

  • Jeffrey D. Wells

    (Florida International University)

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.

Suggested Citation

  • Lynn R. LaMotte & Jeffrey D. Wells, 2016. "Inverse prediction for multivariate mixed models with standard software," Statistical Papers, Springer, vol. 57(4), pages 929-938, December.
    • Handle: RePEc:spr:stpapr:v:57:y:2016:i:4:d:10.1007_s00362-016-0815-2
    DOI: 10.1007/s00362-016-0815-2
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    References listed on IDEAS

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    1. Rolf Sundberg, 1999. "Multivariate Calibration — Direct and Indirect Regression Methodology," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(2), pages 161-207, June.
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    Cited by:

    1. Bhaumik, Dulal K. & Nordgren, Rachel K., 2019. "Prediction and calibration for multiple correlated variables," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 313-327.

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