Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions

Warisa Thangjai; Sa-Aat Niwitpong; Suparat Niwitpong

doi:10.7717/peerj.14498

Estimation of common percentile of rainfall datasets in Thailand using delta-lognormal distributions

PeerJ. 2022 Dec 7:10:e14498. doi: 10.7717/peerj.14498. eCollection 2022.

Authors

Warisa Thangjai¹, Sa-Aat Niwitpong², Suparat Niwitpong²

Affiliations

¹ Department of Statistics, Faculty of Science, Ramkhamhaeng University, Bangkok, Thailand.
² Department of Applied Statistics, Faculty of Applied Science, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand.

Abstract

Weighted percentiles in many areas can be used to investigate the overall trend in a particular context. In this article, the confidence intervals for the common percentile are constructed to estimate rainfall in Thailand. The confidence interval for the common percentile help to indicate intensity of rainfall. Herein, four new approaches for estimating confidence intervals for the common percentile of several delta-lognormal distributions are presented: the fiducial generalized confidence interval, the adjusted method of variance estimates recovery, and two Bayesian approaches using fiducial quantity and approximate fiducial distribution. The Monte Carlo simulation was used to evaluate the coverage probabilities and average lengths via the R statistical program. The proposed confidence intervals are compared in terms of their coverage probabilities and average lengths, and the results of a comparative study based on these metrics indicate that one of the Bayesian confidence intervals is better than the others. The efficacies of the approaches are also illustrated by applying them to daily rainfall datasets from various regions in Thailand.

Keywords: Adjusted method of variance estimates recovery; Average lengths; Bayesian approaches; Common percentile; Confidence intervals; Coverage probability; Fiducial generalized confidence interval; Simulation.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Computer Simulation
Models, Statistical*
Probability
Thailand

Grants and funding

This research was funded by King Mongkut’s University of Technology North Bangkok. Grant No, KMUTNB-66-KNOW-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.