Department of Mathematics, Uttaradit Rajabhat University, Uttaradit, Thailand.
Department of Applied Statistics, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
PLoS One. 2021 Jul 6;16(7):e0253935. doi: 10.1371/journal.pone.0253935. eCollection 2021.
Natural disasters such as flooding and landslides are important unexpected events during the rainy season in Thailand, and how to direct action to avoid their impacts is the motivation behind this study. The differences between the means of natural rainfall datasets in different areas can be estimated using simultaneous confidence intervals (SCIs) for pairwise comparisons of the means of delta-lognormal distributions. Our proposed methods are based on a parametric bootstrap (PB), a fiducial generalized confidence interval (FGCI), the method of variance estimates recovery (MOVER), and Bayesian credible intervals based on mixed (BCI-M) and uniform (BCI-U) priors. Their coverage probabilities, lower and upper error probabilities, and relative average lengths were used to evaluate and compare their SCI performances through Monte Carlo simulation. The results show that BCI-U and PB work well in different situations, even with large differences in variances [Formula: see text]. All of the methods were applied to estimate pairwise differences between the means of natural rainfall data from five areas in Thailand during the rainy season to determine their abilities to predict occurrences of flooding and landslides.
自然灾害如洪水和山体滑坡是泰国雨季期间的重要意外事件，如何指导行动以避免其影响是本研究的动机。可以使用 delta-lognormal 分布均值的成对比较的同时置信区间 (SCIs) 估计不同地区自然降雨数据集之间的均值差异。我们提出的方法基于参数引导 (PB)、基准广义置信区间 (FGCI)、方差估计恢复法 (MOVER)，以及基于混合 (BCI-M) 和均匀 (BCI-U) 先验的贝叶斯可信区间。通过蒙特卡罗模拟，使用它们的覆盖率概率、下误差概率和上误差概率以及相对平均长度来评估和比较它们的 SCI 性能。结果表明，BCI-U 和 PB 在不同情况下表现良好，即使方差差异很大 [公式：见正文]。所有方法都应用于估计泰国雨季期间五个地区的自然降雨数据均值的两两差异，以确定它们预测洪水和山体滑坡发生的能力。
