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A unified and generalized set of shrinkage bounds on minimax Stein estimates

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  • Fourdrinier, Dominique
  • Strawderman, William E.

Abstract

Consider the problem of estimating the mean vector [theta] of a random variable X in , with a spherically symmetric density f(||x-[theta]||2), under loss ||[delta]-[theta]||2. We give an increasing sequence of bounds on the shrinkage constant of Stein-type estimators depending on properties of f(t) that unify and extend several classical bounds from the literature. The basic way to view the conditions on f(t) is that the distribution of X arises as the projection of a spherically symmetric vector (X,U) in . A second way is that f(t) satisfies (-1)jf(j)(t)>=0 for 0

Suggested Citation

  • Fourdrinier, Dominique & Strawderman, William E., 2008. "A unified and generalized set of shrinkage bounds on minimax Stein estimates," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2221-2233, November.
    • Handle: RePEc:eee:jmvana:v:99:y:2008:i:10:p:2221-2233
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    References listed on IDEAS

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    1. Cellier, D. & Fourdrinier, D., 1995. "Shrinkage Estimators under Spherical Symmetry for the General Linear Model," Journal of Multivariate Analysis, Elsevier, vol. 52(2), pages 338-351, February.
    2. Fourdrinier, Dominique & Ouassou, Idir & Strawderman, William E., 2003. "Estimation of a parameter vector when some components are restricted," Journal of Multivariate Analysis, Elsevier, vol. 86(1), pages 14-27, July.
    3. Strawderman, William E., 1974. "Minimax estimation of location parameters for certain spherically symmetric distributions," Journal of Multivariate Analysis, Elsevier, vol. 4(3), pages 255-264, September.
    4. Brandwein, Ann Cohen, 1979. "Minimax estimation of the mean of spherically symmetric distributions under general quadratic loss," Journal of Multivariate Analysis, Elsevier, vol. 9(4), pages 579-588, December.
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    1. Fourdrinier, Dominique & Strawderman, William, 2014. "On the non existence of unbiased estimators of risk for spherically symmetric distributions," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 6-13.

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