基于非对称平方对数误差损失记录的指数分布收缩估计

M. N. Qomi, L. Barmoodeh
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引用次数: 8

摘要

本文研究了在非对称平方对数误差损失函数下,基于记录数据对指数分布中未知尺度参数θ > 0的收缩估计。在形式为cTm的估计量类中导出了一个最小风险无偏估计量,其中Tm是θ的最大似然估计。提出了几种收缩指标,并对其风险进行了计算。在平方对数误差损失函数下,计算了相对于最小风险无偏估计量cTm形式的收缩估计量的相对效率,以进行比较。最后给出了一个实例。
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Shrinkage Testimation in Exponential Distribution based on Records under Asymmetric Squared Log Error Loss
In the present paper, we study shrinkage testimation for the unknown scale parameter θ > 0 of the exponential distribution based on record data under the asymmetric squared log error loss function. A minimum risk unbiased estimator within the class of the estimators of the form cTm is derived, where Tm is the maximum likelihood estimate of θ. Some shrinkage testimators are proposed and their risks are computed. The relative efficiencies of the shrinkage testimators with respect to a minimum risk unbiased estimator of the form cTm under the squared log error loss function are calculated for the comparison purposes. An illustrative example is also presented.
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