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Spectral PCA for MANOVA and data over binary trees

Author

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  • Speed, Terence P.
  • Hicks, Damien G.

Abstract

We extend a concept of ANOVA broader than the traditional variance component models to MANOVA. Within this framework we can derive a spectral principal component analysis (PCA) and see how it generalises the same notion for weakly stationary vector time series. We then attempt to obtain analogous results for arrays of random variables over (i.e., indexed by the nodes of) binary trees, with only partial success. While there is an analogue of ANOVA and MANOVA for binary trees, the existence of spectral PCA there is unresolved.

Suggested Citation

  • Speed, Terence P. & Hicks, Damien G., 2022. "Spectral PCA for MANOVA and data over binary trees," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
  • Handle: RePEc:eee:jmvana:v:188:y:2022:i:c:s0047259x21001780
    DOI: 10.1016/j.jmva.2021.104905
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    References listed on IDEAS

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    1. Mathew, Thomas, 1989. "MANOVA in the multivariate components of variance model," Journal of Multivariate Analysis, Elsevier, vol. 29(1), pages 30-38, April.
    2. Damien G Hicks & Terence P Speed & Mohammed Yassin & Sarah M Russell, 2019. "Maps of variability in cell lineage trees," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-32, February.
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    More about this item

    Keywords

    ANOVA; MANOVA; PCA; Spectral PCA;
    All these keywords.

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