• Journal of the Korean Statistical Society
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• v.19 no.2
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• pp.176-181
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• 1990

Sung [1990] presented an analogue of the classical Cramer-Rao inequality for median-unbiased estimators with continuous multivariate densities depending upon a vector parameter. In the process, diffusivity, a new dispersion measure relevant to median-unbiased estimators, was defined to be a function of median-unbiased estimator's density height. In this paper we shall elaborate these ideas by defining a second kind of diffusivity and discuss the role of model-unbiasedness in median-unbiased estimation in connection with this seconde kind of diffusivity. In addition, median-unbiased estimation will be compared to mean-unbiased estimation.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.13-20
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• 2004

A more general version of diffusivity based on total variation of density is defined and an information inequality for median-unbiased estimation is presented. The resulting information inequality can be interpreted as an analogue of the Bhattacharyya system of lower bounds for mean-unbiased estimation. A condition on which the information bound is achieved is also given.

• Journal of the Korean Statistical Society
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• v.22 no.1
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• pp.1-12
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• 1993

An approach to make the information bound sharper in median-unbiased estimation, based on an analogue of the Cramer-Rao inequality developed by Sung et al. (1990), is introduced for continuous densities with a nuisance parameter by considering information quantities contained both in the parametric function of interest and in the nuisance parameter in a linear fashion. This approach is comparable to that of improving the information bound in mean-unbiased estimation for the case of two unknown parameters. Computation of an optimal weight corresponding to the nuisance parameter is also considered.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.445-452
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• 1997

A more generalized version of the information inequality based on diffusivity which is a natural measure of dispersion for median-unbiased estimators developed by Sung et al. (1990) is presented. This non-Bayesian L$_{1}$ information inequality is free from regularity conditions and can be regarded as an analogue of the Chapman-Robbins inequality for mean-unbiased estimation. The approach given here, however, deals with a more generalized situation than that of the Chapman-Robbins inequality. We also develop a Bayesian version of the L$_{1}$ information inequality in median-unbiased estimation. This latter inequality is directly comparable to the Bayesian Cramer-Rao bound due to the van Trees inequality.