Simultaneous confidence intervals for all pairwise differences between the coefficients of variation of rainfall series in Thailand

Yosboonruang, Noppadon; Niwitpong, Sa-Aat; Niwitpong, Suparat

doi:10.7717/peerj.11651

Cited by 6 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this simulation study, we conducted a performance comparison of four methods, which are denoted as FGCI, HPD.J, HPD.JR, and HPD.U, as described in equations ( 4), ( 6), (7), and (8), respectively. This comparison was carried out using the statistical software R through Monte Carlo simulations.…”

Section: Resultsmentioning

confidence: 99%

Simultaneous Confidence Intervals for Ratios of the Percentiles of the Delta-Lognormal Distribution

Janthasuwan,

Niwitpong,

Niwitpong

2024

Mal. J. Fund. Appl. Sci.

View full text Add to dashboard Cite

Statistical percentiles are extremely important and valuable measurement tools in various contexts. They offer significant advantages when it comes to estimating a wide range of values across different fields, making them essential in statistical analysis and research. This importance motivates researchers to study the construction of simultaneous confidence intervals for ratios of percentiles of the delta-lognormal distribution. This research introduces four distinct methods for constructing such confidence intervals: the fiducial generalized confidence interval, the Bayesian-based highest posterior density credible interval using Jeffreys prior, Jeffreys rule prior, and Uniform prior. All methods are subjected to evaluation and comparison through coverage probabilities and expected lengths in Monte Carlo simulations. The simulation study shows that, overall, the Bayesian-based highest posterior density credible interval works better and more accurately than the fiducial generalized confidence interval. To further validate the findings of the simulation study, these methods are applied to real-world daily rainfall data collected from a river basin in Thailand.

show abstract

Section: Resultsmentioning

confidence: 99%

Simultaneous Confidence Intervals for Ratios of the Percentiles of the Delta-Lognormal Distribution

Janthasuwan,

Niwitpong,

Niwitpong

2024

Mal. J. Fund. Appl. Sci.

View full text Add to dashboard Cite

show abstract

“…Abdel-Karim [18] suggested the MOVER method for constructing the SCI for the ratios of the means of multiple lognormal distributions. Yosboonruang et al [19] provided an SCI for all pairwise differences among the CVs of delta-lognormal distributions by employing the fiducial GCI, Bayesian, and MOVER methods. La-ongkaew et al [20] constructed the SCI for differences in the means of several Weibull distributions by utilizing the GCI, MOVER, and Bayesian approaches.…”

Section: Introductionmentioning

confidence: 99%

The Simultaneous Confidence Interval for the Ratios of the Coefficients of Variation of Multiple Inverse Gaussian Distributions and Its Application to PM2.5 Data

Chankham,

Niwitpong,

Niwitpong

2024

Symmetry

Self Cite

View full text Add to dashboard Cite

Due to slash/burn agricultural activity and frequent forest fires, PM2.5 has become a significant air pollution problem in Thailand, especially in the north and north east regions. Since its dispersion differs both spatially and temporally, estimating PM2.5 concentrations discretely by area, for which the inverse Gaussian distribution is suitable, can provide valuable information. Herein, we provide derivations of the simultaneous confidence interval for the ratios of the coefficients of variation of multiple inverse Gaussian distributions using the generalized confidence interval, the Bayesian interval based on the Jeffreys’ rule prior, the fiducial interval, and the method of variance estimates recovery. The efficacies of these methods were compared by considering the coverage probability and average length obtained from simulation results of daily PM2.5 datasets. The findings indicate that in most instances, the fiducial method with the highest posterior density demonstrated a superior performance. However, in certain scenarios, the Bayesian approach using the Jeffreys’ rule prior for the highest posterior density yielded favorable results.

show abstract

“…Simulation results indicated that the GCI approach yielded satisfactory performance across all cases, while the MOVER approach was preferable for large sample sizes. Yosboonruang et al, [22] focused on constructing SCIs for pairwise differences of CVs in the delta-lognormal populations. They employed three methods: fiducial generalized CI, Bayesian analysis, and the MOVER.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Simultaneous Credible Intervals for the Differences of Coefficients of Variation of Weibull Distributions with Application to Wind Speed Data in Thailand

Manussaya La-ongkaew,

Sa-Aat Niwitpong,

Suparat Niwitpong

2024

ARFMTS

View full text Add to dashboard Cite

The Weibull distribution serves as a versatile tool for modelling various types of data, including reliability analysis, failure rates, survival analysis, and extreme value analysis. Specifically, in weather forecasting, the Weibull distribution is frequently used to represent variables such as wind speed. To examine differences in wind speed dispersion across different areas, the coefficient of variation proves optimal. However, when comparing variations across multiple areas simultaneously, the focus shifts to simultaneous confidence intervals (SCIs). Therefore, this paper focuses on the problem of constructing the SCIs for the differences of coefficients of variation derived from Weibull distributions. The proposed methods in this study are Bayesian approaches that utilize gamma and uniform prior distributions, namely the generalized confidence interval (GCI) and the method of variance estimate recovery (MOVER) based on the Hendricks and Robey interval. To assess the performance of these methods, their coverage probability, expected length, and standard errors were evaluated through a simulation study. The simulation results show that the Bayesian credible interval based on the gamma prior performs satisfactorily in most cases. Finally, to illustrate the SCIs, we present an example concerning wind speed dispersion data in Southern Thailand.

show abstract

Simultaneous confidence intervals for all pairwise differences between the coefficients of variation of rainfall series in Thailand

Cited by 6 publications

References 38 publications

Simultaneous Confidence Intervals for Ratios of the Percentiles of the Delta-Lognormal Distribution

Simultaneous Confidence Intervals for Ratios of the Percentiles of the Delta-Lognormal Distribution

The Simultaneous Confidence Interval for the Ratios of the Coefficients of Variation of Multiple Inverse Gaussian Distributions and Its Application to PM2.5 Data

Bayesian Simultaneous Credible Intervals for the Differences of Coefficients of Variation of Weibull Distributions with Application to Wind Speed Data in Thailand

Contact Info

Product

Resources

About