Maneerat Patcharee, Niwitpong Sa-Aat, Niwitpong Suparat
Department of Applied Statistics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
PeerJ. 2020 Feb 11;8:e8502. doi: 10.7717/peerj.8502. eCollection 2020.
Natural disasters such as drought and flooding are the consequence of severe rainfall fluctuation, and rainfall amount data often contain both zero and positive observations, thus making them fit a delta-lognormal distribution. By way of comparison, rainfall dispersion may not be similar in enclosed regions if the topography and the drainage basin are different, so it can be evaluated by the ratio of variances. To estimate this, credible intervals using the highest posterior density based on the normal-gamma prior (HPD-NG) and the method of variance estimates recovery (MOVER) for the ratio of delta-lognormal variances are proposed. Monte Carlo simulation was used to assess the performance of the proposed methods in terms of coverage probability and relative average length. The results of the study reveal that HPD-NG performed very well and was able to meet the requirements in various situations, even with a large difference between the proportions of zeros. However, MOVER is the recommended method for equal small sample sizes. Natural rainfall datasets for the northern and northeastern regions of Thailand are used to illustrate the practical use of the proposed credible intervals.
干旱和洪水等自然灾害是严重降雨波动的结果，降雨量数据通常同时包含零观测值和正观测值，因此使其符合δ-对数正态分布。相比之下，如果地形和流域不同，封闭区域内的降雨离散度可能不相似，因此可以通过方差比来评估。为了估计这一点，提出了基于正态-伽马先验的最高后验密度可信区间（HPD-NG）和δ-对数正态方差比的方差估计恢复方法（MOVER）。使用蒙特卡罗模拟从覆盖概率和相对平均长度方面评估所提方法的性能。研究结果表明，HPD-NG表现非常出色，即使零比例之间存在很大差异，也能够在各种情况下满足要求。然而，对于相等的小样本量，MOVER是推荐方法。使用泰国北部和东北部地区的自然降雨数据集来说明所提可信区间的实际应用。
