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Estimation of a covariance matrix in multivariate skew-normal distribution

Author

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  • Hisayuki Tsukuma
  • Tatsuya Kubokawa

Abstract

This article addresses the problem of estimating a covariance matrix in a multivariate skew-normal distribution relative to two different losses. The estimation problem can be reduced to that of a scale matrix of a noncentral Wishart distribution. The noncentrality parameter matrix, which is a nuisance parameter, brings about non optimality of the best triangular invariant estimators which are minimax under normality. Some improving techniques under normality are proven to remain robust under the multivariate skew-normal distribution.

Suggested Citation

  • Hisayuki Tsukuma & Tatsuya Kubokawa, 2020. "Estimation of a covariance matrix in multivariate skew-normal distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(5), pages 1174-1200, March.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:5:p:1174-1200
    DOI: 10.1080/03610926.2018.1554137
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