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Minimax Estimators for the Location Vectors of Spherically Symmetric Densities

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  • Judge, George
  • Miyazaki, Shigetaka
  • Yancey, Thomas

Abstract

The estimation of K (K ≥ 3) location parameters is considered under quadratic loss when the coordinates of the best invariant estimators are spherically symmetrically distributed. Under these stochastic mechanisms traditional Stein estimators are evaluated for finite samples and shown to have a risk performance superior to some conventional rules.

Suggested Citation

  • Judge, George & Miyazaki, Shigetaka & Yancey, Thomas, 1985. "Minimax Estimators for the Location Vectors of Spherically Symmetric Densities," Econometric Theory, Cambridge University Press, vol. 1(3), pages 409-417, December.
    • Handle: RePEc:cup:etheor:v:1:y:1985:i:03:p:409-417_01
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    Cited by:

    1. Akio Namba & Kazuhiro Ohtani, 2007. "Risk comparison of the Stein-rule estimator in a linear regression model with omitted relevant regressors and multivariatet errors under the Pitman nearness criterion," Statistical Papers, Springer, vol. 48(1), pages 151-162, January.
    2. Akio Namba, 2001. "MSE performance of the 2SHI estimator in a regression model with multivariate t error terms," Statistical Papers, Springer, vol. 42(1), pages 81-96, January.

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