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Confidence Intervals of the Inverse of Coefficient of Variation of Delta-Gamma Distribution

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Abstract

The inverse of the coefficient of variation (ICV), otherwise known as the signal to-noise ratio, is the ratio of the population standard deviation to the population mean. It has often been used in the fields of finance and image processing, among others. In this study, various methods were applied to estimate the confidence intervals (CIs) for the difference between and the ratio of the ICVs of two delta-gamma distributions. The fiducial quantity method, Bayesian CI estimates based on the Jeffreys, uniform, or normal-gamma-beta (NGB) prior, and highest posterior density (HPD) intervals based on the Jeffreys, uniform, or NGB priors were used in this endeavor. A Monte Carlo simulation study was conducted to assess the performances of the proposed CI estimation methods in terms of their coverage probabilities and average lengths. The results indicate that the HPD interval based on the NGB prior or the Jeffreys prior performed well for a small probability of the samples containing zero observations (\(\delta\)) whereas the fiducial quantity method performed well for large values of \(\delta\). Furthermore, we demonstrate the practicability of the proposed methods using rainfall data from Lampang province, Thailand.

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REFERENCES

  1. G. Casella and R. L. Berger, Statistical Inference, 2nd ed. (Cengage Learning, US, 2001).

    Google Scholar 

  2. T. Holgersson, P. Karlsson, and R. Mansoor, ‘‘Estimating mean-standard deviation ratios of financial data,’’ J. Appl. Stat. 39, 657–671 (2013).

    Article  MathSciNet  Google Scholar 

  3. A. N. Albatineh, B. M. G. Kibria, and B. Zogheib, ‘‘Asymptotic sampling distribution of inverse coefficient of variation and its applications,’’ J. Adv. Stat. Probab. 2, 15–20 (2014).

    Google Scholar 

  4. A. N. Albatineh, I. Boubakari, and B. M. G. Kibria, ‘‘New confidence interval estimator of the signal to noise ratio based on asymptotic sampling distribution,’’ Commun. Stat. Theory Methods 46, 574–590 (2017).

    Article  MathSciNet  Google Scholar 

  5. F. George and B. M. G. Kibria, ‘‘Confidence intervals for estimating the population signal to noise ratio: A simulation study,’’ J. Appl. Stat. 39, 1225–1240 (2012).

    Article  MathSciNet  Google Scholar 

  6. K. K. Sharma and H. Krishna, ‘‘Asymptotic sampling distribution of inverse coefficient of variation and its applications,’’ IEEE Trans. Reliab. 43, 630–633 (1994).

    Article  Google Scholar 

  7. S. Niwitpong, ‘‘Confidence intervals for functions of signal-to-noise ratios of normal distributions,’’ Studies Comput. Intell. 760, 255–265 (2018).

    Article  Google Scholar 

  8. L. Saothayanun and W. Thangjai, ‘‘Confidence intervals for the signal to noise ratio of two-parameter exponential distribution,’’ IEEE Trans. Reliab. 43, 630–633 (2018).

    Google Scholar 

  9. W. Thangjai and S. Niwitpong, ‘‘Confidence intervals for the signal-to-noise ratio and difference of signal-to-noise ratios of gamma distributions,’’ Adv. Appl. Math. Sci. 18, 503–520 (2019).

    Google Scholar 

  10. W. Thangjai and S. Niwitpong, ‘‘Confidence intervals for the signal-to-noise ratio and difference of signal-to-noise ratios of lognormal distribution,’’ Stats 2, 164–173 (2019).

    Article  Google Scholar 

  11. W. Thangjai and S. Niwitpong, ‘‘Confidence intervals for common signal-to-noise ratio of several log-normal distributions,’’ Iran. J. Sci. Technol. Trans., A: Sci. 44, 99–107 (2020).

    Article  MathSciNet  Google Scholar 

  12. W. Thangjai and S. Niwitpong, ‘‘Confidence intervals for difference of signal-to-noise ratios of two-parameter exponential distributions,’’ Int. J. Stat. Appl. Math. 5 (3), 47–54 (2020).

    Google Scholar 

  13. X. Wang, C. Zou, L. Yi, J. Wang, and X. Li, ‘‘Fiducial inference for gamma distributions: Two-sample problems,’’ Commun. Stat. – Simul. Comput. 50, 811–821 (2019).

    Article  MathSciNet  Google Scholar 

  14. J. Aitchison, ‘‘On the distribution of a positive random variable having a discrete probability mass at the origin,’’ J. Am. Stat. Assoc. 50 (271), 901 (1955).

    MathSciNet  Google Scholar 

  15. J. Aitchison and J. A. C. Brown, The Lognormal Distribution: With Special Reference to its Uses in Economics (Cambridge University Press, London, UK, 1963).

    Google Scholar 

  16. N. Yosboonruang, S. A. Niwitpong, and S. Niwitpong, ‘‘Measuring the dispersion of rainfall using Bayesian confidence intervals for coefficient of variation of delta-lognormal distribution: A study from Thailand,’’ PeerJ 7, e7344 (2019).

  17. P. Maneerat, S. A. Niwitpong, and S. Niwitpong, ‘‘Bayesian approach to construct confidence intervals for comparing the rainfall dispersion in Thailand,’’ PeerJ 8, e8502 (2020).

  18. P. Maneerat, S. A. Niwitpong, and S. Niwitpong, ‘‘Bayesian confidence intervals for the difference between variances of delta-lognormal distributions,’’ Biometr. J. 62, 1769–1790 (2020).

    Article  MathSciNet  Google Scholar 

  19. P. Maneerat, S. A. Niwitpong, and S. Niwitpong, ‘‘Estimating the average daily rainfall in Thailand using confidence intervals for the common mean of several delta-lognormal distributions,’’ PeerJ 9, e10758 (2021).

  20. P. Maneerat, S. A. Niwitpong, and S. Niwitpong, ‘‘Bayesian confidence intervals for variance of delta-lognorma distribution with an application to rainfall dispersion,’’ Stat. Interface 14, 229–241 (2021).

    Article  MathSciNet  Google Scholar 

  21. Q. Zhang, J. Xu, J. Zhao, H. Liang, and X. Li, ‘‘Simultaneous confidence intervals for ratios of means of zero-inflated log-normal populations,’’ J. Stat. Comput. Simul. 92, 1113–1132 (2022).

    Article  MathSciNet  Google Scholar 

  22. P. Ren, G. Lui, and X. Pu, ‘‘Simultaneous confidence intervals for mean differences of multiple zero-inflated gamma distributions with applications to precipitation,’’ Commun. Stat. – Simul. Comput. 52, 4705 (2021).

    Article  MathSciNet  Google Scholar 

  23. K. Muralidharan and B. K. Kale, ‘‘Modified gamma distributions with singularity at zero,’’ Commun. Stat. – Simul. Comput. 31, 143–158 (2002).

    Article  MathSciNet  Google Scholar 

  24. J. B. Lecomte, H. P. Benot, S. Ancelet, M. P. Etienne, L. Bel, and E. Parent, ‘‘Compound Poisson-gamma vs. delta-gamma to handle zero-inflated continuous data under a variable sampling volume,’’ Methods Ecol. Evol. 4, 1159–1166 (2013).

    Article  Google Scholar 

  25. T. Kaewprasert, S. A. Niwitpong, and S. Niwitpong, ‘‘Bayesian estimation for the mean of delta-gamma distributions with application to rainfall data in Thailand,’’ PeerJ 10, 1–27 (2022).

    Article  Google Scholar 

  26. W. Khooriphan, S. A. Niwitpong, and S. Niwitpong, ‘‘Bayesian estimation of rainfall dispersion in Thailand using gamma distribution with excess zeros,’’ PeerJ 10, e14023 (2022).

  27. P. Sangnawakij and S. A. Niwitpong, ‘‘Confidence intervals for functions of coefficients of variation with bounded parameter spaces in two gamma distributions,’’ Songklanakarin J. Sci. Technol. 39, 27–39 (2017).

    Google Scholar 

  28. K. Krishnamoorthy and X. Wang, ‘‘Fiducial confidence limits and prediction limits for a gamma distribution: Censored and uncensored cases,’’ Environmetrics 27, 479–493 (2016).

    Article  MathSciNet  Google Scholar 

  29. X. Li, X. Zhou, and L. Tian, ‘‘Interval estimation for the mean of lognormal data with excess zeros,’’ Stat. Probab. Lett. 83, 2447–2453 (2013).

    Article  MathSciNet  Google Scholar 

  30. W. M. Bolstad and J. M. Curran, Introduction to Bayesian Statistics, 3rd ed. (Wiley, Hoboken, 2016).

    Google Scholar 

  31. H. Jeffreys, Theory of Probability (Oxford Univ. Press, UK, 1961).

    Google Scholar 

  32. T. A. Kalkur and A. Rao, ‘‘Bayes estimator for coefficient of variation and inverse coefficient of variation for the normal distribution,’’ Int. J. Stat. Syst. 12, 721–732 (2017).

    Google Scholar 

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Funding

The first author would like to express gratitude to the Thai Scientific Achievement Scholarship (SAST) for financial assistance. This research was supported by the National Science, Research, and Innovation Fund (NSRF) and King Mongkut’s University of Technology North Bangkok (Grant no. KMUTNB-FF-66-44).

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Correspondence to Wansiri Khooriphan, Sa-Aat Niwitpong or Suparat Niwitpong.

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Khooriphan, W., Niwitpong, SA. & Niwitpong, S. Confidence Intervals of the Inverse of Coefficient of Variation of Delta-Gamma Distribution. Lobachevskii J Math 44, 4739–4762 (2023). https://doi.org/10.1134/S1995080223110227

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  • DOI: https://doi.org/10.1134/S1995080223110227

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