首页> 外文期刊>Reliability, IEEE Transactions on >Point and Interval Estimation, From One-Order Statistic, of the Location Parameter of an Extreme-Value Distribution with Known Scale Parameter and of the Scale Parameter of a Weibull Distribution with Known Shape Parameter
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Point and Interval Estimation, From One-Order Statistic, of the Location Parameter of an Extreme-Value Distribution with Known Scale Parameter and of the Scale Parameter of a Weibull Distribution with Known Shape Parameter

机译:一阶统计量的点和间隔估计,具有已知比例参数的极值分布的位置参数和具有已知形状参数的Weibull分布的比例参数

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This paper derives a one-order statistic estimator ????????mn b for the location parameter of the (first) extreme-value distribution of smallest values with cumulative distribution function F(x;u,b) = 1 - exp {-exp[(x-u)/b]} using the minimum-variance unbiased one-order statistic estimator for the scale parameter of an exponential distribution, as was done in an earlier paper for the scale parameter of a Weibull distribution. It is shown that exact confidence bounds, based on one-order statistic, can be easily derived for the location parameter of the extreme-value distribution and for the scale parameter of the Weibull distribution, using exact confidence bounds for the scale parameter of the exponential distribution. The estimator for u is shown to be b ln cmn + xmn, where xmn is the mth order statistic from an ordered sample of size n from the extreme-value distribution with scale parameter b and Cmn is the coefficient for a one-order statistic estimator of the scale parameter of an exponential distribution. Values of the factor cmn, which have previously viously been tabulated for n = 1(1)20, are given for n = 21(1)40. The ratios of the mean-square-errors of the maximum-likelihood estimators based on m order statistics to those of the one-order statistic estimators for the location parameter of the extreme-value distribution and the scale parameter of the Weibull distribution are investigated by Monte Carlo methods. The use of the table and related tables is discussed and illustrated by numerical examples.
机译:本文针对累积分布函数F(x; u,b)= 1-的最小值的(第一)极值分布的位置参数导出一阶统计估计量mnb。 exp {-exp [(xu)/ b]}使用指数分布的比例参数的最小方差无偏一阶统计估计量,就像在早期论文中对Weibull分布的比例参数所做的那样。结果表明,使用一阶统计量的精确置信范围,可以使用指数分布比例参数的精确置信范围,轻松得出极值分布的位置参数和威布尔分布的比例参数。分配。 u的估计量显示为b ln cmn + xmn,其中xmn是尺度参数为b的极值分布的大小为n的有序样本的m阶统计量,Cmn是一阶统计量估计量的系数指数分布的比例参数。对于n = 1(1)20,以前列出了因子cmn的值,之前已将其列出。研究了基于m阶统计量的极大似然估计量的均方误差与一阶统计量估计量的极值分布的位置参数和Weibull分布的比例参数之比。蒙特卡洛方法。通过数值示例来讨论和说明该表和相关表的使用。

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  • 来源
    《Reliability, IEEE Transactions on》 |1966年第3期|共7页
  • 作者单位

    Air Force Institute of Technology and Aerospace Research Laboratories, Wright-Patterson Air Force Base, Ohio.;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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