最优排序集抽样设计下指数分布中总体均值极大似然估计的大样本性质及其渐近区间估计

Large sample properties and asymptotic interval estimation of maximum likelihood estimation of population mean in exponential distribution under optimal ranked set sampling design

  • 摘要: 本文在拟充分完全统计量排序集抽样(RSS)下研究了指数分布中总体均值的极大似然估计(MLE)的大样本性质及其渐近区间估计.为进一步提高统计推断的效率,提出了Fisher信息量最大化RSS设计,并在这种设计下研究了指数分布中总体均值的MLE的大样本性质及其渐近区间估计.数值结果表明,拟充分完全统计量RSS渐近区间估计和Fisher信息量最大化RSS渐近区间估计的精度高于RSS渐近区间估计.Fisher信息量最大化RSS渐近区间估计的精度高于拟充分完全统计量RSS渐近区间估计.

     

    Abstract: This paper investigates the large sample property and asymptotic interval estimation of maximum likelihood estimation (MLE) of population mean in exponential distribution under quasi-sufficient complete statistic ranked set sampling (RSS). In order to improve the effciency of statistical inference, a Fisher information maximization RSS design is proposed, and the large sample property and asymptotic interval estimation of maximum likelihood estimation of population mean in exponential distribution is studied under this design. The numerical results show that the precision of the quasi-sufficient complete statistic RSS asymptotic interval estimation and the Fisher information maximization RSS asymptotic interval estimation are higher than RSS asymptotic interval estimation. The precision of the Fisher information maximization RSS asymptotic interval estimation is higher than the quasi-sufficient complete statistic RSS asymptotic interval estimation.

     

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