Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Graduate Institute of Management, Chang Gung University, Taoyuan, Taiwan.
PLoS One. 2022 Aug 17;17(8):e0271163. doi: 10.1371/journal.pone.0271163. eCollection 2022.
This paper presents a new approach to constructing the confidence interval for the mean value of a population when the distribution is unknown and the sample size is small, called the Percentile Data Construction Method (PDCM). A simulation was conducted to compare the performance of the PDCM confidence interval with those generated by the Percentile Bootstrap (PB) and Normal Theory (NT) methods. Both the convergence probability and average interval width criterion are considered when seeking to find the best interval. The results show that the PDCM outperforms both the PB and NT methods when the sample size is less than 30 or a large population variance exists.
本文提出了一种新的方法来构建当分布未知且样本量较小时的总体均值的置信区间，称为百分位数数据构建方法（PDCM）。通过模拟比较了 PDCM 置信区间与百分位 bootstrap（PB）和正态理论（NT）方法生成的置信区间的性能。在寻找最佳区间时，同时考虑了收敛概率和平均区间宽度准则。结果表明，当样本量小于 30 或总体方差较大时，PDCM 优于 PB 和 NT 方法。
