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Pseudo-inverse multivariate/matrix-variate distributions

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  • Zhang, Zhihua
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    Abstract

    The Moore-Penrose inverse of a singular or nonsquare matrix is not only existent but also unique. In this paper, we derive the Jacobian of the transformation from such a matrix to the transpose of its Moore-Penrose inverse. Using this Jacobian, we investigate the distribution of the Moore-Penrose inverse of a random matrix and propose the notion of pseudo-inverse multivariate/matrix-variate distributions. For arbitrary multivariate or matrix-variate distributions, we can develop the corresponding pseudo-inverse distributions. In particular, we present pseudo-inverse multivariate normal distributions, pseudo-inverse Dirichlet distributions, pseudo-inverse matrix-variate normal distributions and pseudo-inverse Wishart distributions.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 98 (2007)
    Issue (Month): 8 (September)
    Pages: 1684-1692

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    Handle: RePEc:eee:jmvana:v:98:y:2007:i:8:p:1684-1692

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    Related research

    Keywords: Matrix-variate distribution The Moore-Penrose generalized inverse Pseudo-inverse multivariate distribution Pseudo-inverse matrix-variate distribution;

    References

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    1. José Díaz-García & Ramón Gutiérrez-Jáimez, 2005. "Functions of singular random matrices with applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 14(2), pages 475-487, December.
    2. Díaz-García, José A. & Jáimez, Ramón Gutierrez & Mardia, Kanti V., 1997. "Wishart and Pseudo-Wishart Distributions and Some Applications to Shape Theory," Journal of Multivariate Analysis, Elsevier, vol. 63(1), pages 73-87, October.
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