Bootstrapping the Stein Variance Estimator

نویسندگان

  • Akio Namba
  • Kazuhiro Ohtani
چکیده

This paper applies the bootstrap methods proposed by Efron (1979) to the Stein variance estimator proposed by Stein (1964). It is shown by Monte Carlo experiments that the parametric bootstrap yields the considerable accurate estimates of mean, standard error and confidence limits of the Stein variance estimator.

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تاریخ انتشار 2002