Multiple comparisons of precipitation variations in different areas using simultaneous confidence intervals for all possible ratios of variances of several zero-inflated lognormal models

PeerJ

article

Patcharee Maneerat¹  Sa-Aat Niwitpong² 

[1] Department of Applied Mathematics, Rajabhat Uttaradit University;Department of Applied Statistics, King Mongkut’s University of Technology North Bangk

关键词: Precipitation variation;    Ratio of variances;    Bayesian approach;    Parametric bootstrap approach;    Simulation;    Rainfall data;    Beta prior;   

DOI  :  10.7717/peerj.12659

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Flash flooding and landslides regularly cause injury, death, and homelessness in Thailand. An advancedwarning system is necessary for predicting natural disasters, and analyzing the variability of daily precipitation might be usable in this regard. Moreover, analyzing the differences in precipitation data among multiple weather stations could be used to predict variations in meteorological conditions throughout the country. Since precipitation data in Thailand follow a zero-inflated lognormal (ZILN) distribution, multiple comparisons of precipitation variation in different areas can be addressed by using simultaneous confidence intervals (SCIs) for all possible pairwise ratios of variances of several ZILN models. Herein, we formulate SCIs using Bayesian, generalized pivotal quantity (GPQ), and parametric bootstrap (PB) approaches. The results of a simulation study provide insight into the performances of the SCIs. Those based on PB and the Bayesian approach via probability matching with the beta prior performed well in situations with a large amount of zero-inflated data with a large variance. Besides, the Bayesian based on the reference-beta prior and GPQ SCIs can be considered as alternative approaches for small-to-large and medium-to-large sample sizes from large population, respectively. These approaches were applied to estimate the precipitation variability among weather stations in lower southern Thailand to illustrate their efficacies.

【 授权许可】

CC BY