research-article

Confidence Intervals for Single Coefficient of Variation of Weibull Distribution

Authors:

Manussaya La-ongkaew,

Sa-Aat Niwitpong,

Suparat NiwitpongAuthors Info & Claims

ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

Article No.: 72, Pages 1 - 6

https://doi.org/10.1145/3387168.3387253

Published: 25 May 2020 Publication History

Abstract

The aim of this paper is to propose the new confidence intervals for single coefficient of variation of Weibull distributions, using the concept of the generalized confidence interval (GCI), the fiducial generalized confidence interval (FGCI), and the bootstrap percentile method. The coverage probabilities and the expected lengths were evaluated via Monte Carlo simulation. The simulation results showed that the coverage probabilities of the GCIs and the FGCIs were greater than or close to the nominal confidence level. Both of them were recommended. The coverage probabilities of confidence intervals based on the bootstrap percentile method were under the nominal confidence level. Regarding the expected lengths, they tended to decrease when the sample size was increased. All proposed confidence intervals were applied to some real world data in this study.

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Cited By

Cao PHe GYang T(2023)TF-Predictor: Transformer-Based Prerouting Path Delay Prediction FrameworkIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.321675242:7(2227-2237)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TCAD.2022.3216752
La-ongkaew MNiwitpong SNiwitpong S(2023)The Bayesian Confidence Intervals for the Coefficient of Variation of a Weibull DistributionAdvances in Automation, Mechanical and Design Engineering10.1007/978-3-031-40070-4_34(417-427)Online publication date: 4-Oct-2023
https://doi.org/10.1007/978-3-031-40070-4_34

Index Terms

Confidence Intervals for Single Coefficient of Variation of Weibull Distribution
1. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic inference problems
      1. Hypothesis testing and confidence interval computation

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ICVISP 2019: Proceedings of the 3rd International Conference on Vision, Image and Signal Processing

August 2019

584 pages

ISBN:9781450376259

DOI:10.1145/3387168

Copyright © 2019 ACM.

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Published: 25 May 2020

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ICVISP 2019

ICVISP 2019: 3rd International Conference on Vision, Image and Signal Processing

August 26 - 28, 2019

BC, Vancouver, Canada

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ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;

Overall Acceptance Rate 186 of 424 submissions, 44%

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Cited By

Cao PHe GYang T(2023)TF-Predictor: Transformer-Based Prerouting Path Delay Prediction FrameworkIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2022.321675242:7(2227-2237)Online publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1109/TCAD.2022.3216752
La-ongkaew MNiwitpong SNiwitpong S(2023)The Bayesian Confidence Intervals for the Coefficient of Variation of a Weibull DistributionAdvances in Automation, Mechanical and Design Engineering10.1007/978-3-031-40070-4_34(417-427)Online publication date: 4-Oct-2023
https://doi.org/10.1007/978-3-031-40070-4_34

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