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On weakly equivariant estimators

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  • M. Shams

    (University of Kashan)

Abstract

In this paper, we shall generalize the concept of equivariance in statistics to “weak equivariance”. Then, we summarize the properties of weakly equivariant estimators and their applications in statistics. At first we characterize the class of all weakly equivariant estimators. Then, we shall consider the concept of cocycles and isovariance, and so we find their connection with weakly equivariant functions. It is natural to restrict attention to the class of weakly equivariant estimator to find minimum risk weakly equivariant estimators. If the group acts in two different ways, we shall find a relation between the minimum risk equivariant and minimum risk weakly equivariant estimator under the old and new group actions. Also we shall introduce a necessary and sufficient condition for the invariance of the loss function under the new action.

Suggested Citation

  • M. Shams, 2021. "On weakly equivariant estimators," Statistical Papers, Springer, vol. 62(4), pages 1611-1650, August.
    • Handle: RePEc:spr:stpapr:v:62:y:2021:i:4:d:10.1007_s00362-019-01149-0
    DOI: 10.1007/s00362-019-01149-0
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    References listed on IDEAS

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    1. Robert Serfling, 2010. "Equivariance and invariance properties of multivariate quantile and related functions, and the role of standardisation," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(7), pages 915-936.
    2. Pauliina Ilmonen & Hannu Oja & Robert Serfling, 2012. "On Invariant Coordinate System (ICS) Functionals," International Statistical Review, International Statistical Institute, vol. 80(1), pages 93-110, April.
    3. Tatsuya Kubokawa & Yoshihiko Konno, 1990. "Estimating the covariance matrix and the generalized variance under a symmetric loss," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(2), pages 331-343, June.
    4. García, Gloria & M. Oller, Josep, 2001. "Minimum Riemannian risk equivariant estimator for the univariate normal model," Statistics & Probability Letters, Elsevier, vol. 52(1), pages 109-113, March.
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