Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand

PeerJ

article

Theerapong Kaewprasert¹  Sa-Aat Niwitpong¹  Suparat Niwitpong¹ 

[1] Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok

关键词: Credible intervals;    Highest posterior density intervals;    Jeffrey’s rule;    Uniform priors;    Fiducial quantities;    Chiang Mai;    Simulation;    Rainfall data;   

DOI  :  10.7717/peerj.13465

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

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.

【 授权许可】

CC BY