Measuring the dispersion of rainfall using Bayesian confidence intervals for coefficient of variation of delta-lognormal distribution: a study from Thailand

PeerJ

article

Noppadon Yosboonruang¹  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: Bayesian method;    Fiducial generalized confidence interval;    Highest posterior density;    Coverage probability;    Delta-lognormal distribution;    Coefficient of variation;   

DOI  :  10.7717/peerj.7344

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Since rainfall data series often contain zero values and thus follow a delta-lognormal distribution, the coefficient of variation is often used to illustrate the dispersion of rainfall in a number of areas and so is an important tool in statistical inference for a rainfall data series. Therefore, the aim in this paper is to establish new confidence intervals for a single coefficient of variation for delta-lognormal distributions using Bayesian methods based on the independent Jeffreys’, the Jeffreys’ Rule, and the uniform priors compared with the fiducial generalized confidence interval. The Bayesian methods are constructed with either equitailed confidence intervals or the highest posterior density interval. The performance of the proposed confidence intervals was evaluated using coverage probabilities and expected lengths via Monte Carlo simulations. The results indicate that the Bayesian equitailed confidence interval based on the independent Jeffreys’ prior outperformed the other methods. Rainfall data recorded in national parks in July 2015 and in precipitation stations in August 2018 in Nan province, Thailand are used to illustrate the efficacy of the proposed methods using a real-life dataset.

【 授权许可】

CC BY