Department of Applied Statistics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.
PeerJ. 2022 May 18;10:e13465. doi: 10.7717/peerj.13465. eCollection 2022.
Precipitation and flood forecasting are difficult due to rainfall variability. The mean of a delta-gamma distribution can be used to analyze rainfall data for predicting future rainfall, thereby reducing the risks of future disasters due to excessive or too little rainfall. In this study, we construct credible and highest posterior density (HPD) intervals for the mean and the difference between the means of delta-gamma distributions by using Bayesian methods based on Jeffrey's rule and uniform priors along with a confidence interval based on fiducial quantities. The results of a simulation study indicate that the Bayesian HPD interval based on Jeffrey's rule prior performed well in terms of coverage probability and provided the shortest expected length. Rainfall data from Chiang Mai province, Thailand, are also used to illustrate the efficacies of the proposed methods.
由于降雨变化，降水和洪水预报具有难度。德尔塔-伽马分布的均值可用于分析降雨数据，从而预测未来的降雨，从而降低因降雨过多或过少而引发未来灾害的风险。在本研究中，我们基于杰弗里法则和均匀先验的贝叶斯方法，结合基于基准量的置信区间，构建了德尔塔-伽马分布均值和均值差值的可信和最高后验密度（HPD）区间。模拟研究结果表明，基于杰弗里法则先验的贝叶斯 HPD 区间在覆盖率和提供最短预期长度方面表现良好。泰国清迈府的降雨数据也用于说明所提出方法的功效。
