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The information domain confidence intervals in univariate linear calibration
Communications in Statistics - Simulation and Computation ( IF 0.9 ) Pub Date : 2020-07-07 , DOI: 10.1080/03610918.2020.1777302
Guimei Zhao 1 , Xingzhong Xu 2, 3
Affiliation  

Abstract

We consider the confidence interval for the univariate linear calibration, where a response variable is related to an explanatory variable by a simple linear model, and the observations of the response variable and known values of the explanatory variable are used to make inferences on a single unknown value of the explanatory variable. Since the univariate linear calibration suffers from a problem of local unidentifiability, which results in the confidence coefficient of every confidence interval with finite length being zero, we propose new confidence intervals in terms of information domain, which are verified to be 1α confidence intervals for a specified range of the interesting parameter. The proposed intervals are numerically compared with two existing methods, and simulations show that our confidence intervals have good behavior in the coverage probability and the expected length. We also illustrate the results using an example.



中文翻译:

单变量线性校准中的信息域置信区间

摘要

我们考虑单变量线性校准的置信区间,其中响应变量通过简单的线性模型与解释变量相关,并且响应变量的观察值和解释变量的已知值用于对单个未知数进行推断解释变量的值。由于单变量线性标定存在局部不可识别问题,导致每个有限长度的置信区间的置信系数为零,我们提出了新的信息域置信区间,经验证为1-α感兴趣参数的指定范围的置信区间。提出的区间与两种现有方法进行了数值比较,仿真表明我们的置信区间在覆盖概率和预期长度方面具有良好的表现。我们还用一个例子来说明结果。

更新日期:2020-07-07
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