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Properties and Parameter Estimation of the Partly-Exponential Distribution

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Abstract

The partly-exponential distribution was introduced in the paper by Atikankul et al. in 2021, but no properties of this distribution have been investigated. In this paper, we derive various theoretical properties such as the cumulative distribution function, the moment generating function, the first three moments, the characteristic function, and the mode. The maximum likelihood estimation is used to estimate the parameters. Moreover, the Wald and the profile likelihood approaches are used for interval estimation. A simulation study is conducted to examine the mean square error and bias of the maximum likelihood estimators, as well as the coverage probability of the both confidence intervals.

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REFERENCES

  1. Y. Atikankul, A. Thongteeraparp, W. Bodhisuwan, J. Qin, and S. Boonto, ‘‘The zero-truncated Poisson-weighted exponential distribution,’’ Lobachevskii J. Math. 42, 3088–3097 (2021).

    Article  MathSciNet  MATH  Google Scholar 

  2. F. B. Hildebrand, Introduction to Numerical Analysis (Courier, New York, 1987).

    MATH  Google Scholar 

  3. S. Akram and Q. U. Ann, ‘‘Newton raphson method,’’ Int. J. Sci. Eng. Res. 6, 1748–1752 (2015).

    Google Scholar 

  4. S. Gratton, A. S. Lawless, and N. K. Nichols, ‘‘Approximate Gauss–Newton methods for nonlinear least squares problems,’’ SIAM J. Optim. 18, 106–132 (2007).

    Article  MathSciNet  MATH  Google Scholar 

  5. Y.-H. Dai, ‘‘A perfect example for the BFGS method,’’ Math. Program. 138, 501–530 (2013).

    Article  MathSciNet  MATH  Google Scholar 

  6. R Core Team and Contributors Worldwide, General-Purpose Optimization, 2022. https://stat.ethz.ch/R-manual/R-devel/library/stats/html/optim.html.

  7. CRAN Team, The comprehensive R archive network, 2023. https://cran.r-project.org/.

  8. A. Ly, M. Marsman, J. Verhagen, R. P. Grasman, and E.-J. Wagenmakers, ‘‘A tutorial on fisher information,’’ J. Math. Psychol. 80, 40–55 (2017).

    Article  MathSciNet  MATH  Google Scholar 

  9. G. Casella, S. Fienberg, and I. Olkin, Elements of Large-Sample Theory (Springer, New York, 1998).

    Google Scholar 

  10. R. Cukier, H. Levine, and K. Shuler, ‘‘Nonlinear sensitivity analysis of multiparameter model systems,’’ J. Comput. Phys. 26, 1–42 (1978).

    Article  MathSciNet  MATH  Google Scholar 

  11. G. Salvadori and C. de Michele, ‘‘Multivariate multiparameter extreme value models and return periods: A copula approach,’’ Water Resour. Res. 46 (2010).

  12. D. J. Venzon and S. H. Moolgavkar, ‘‘A method for computing profile likelihood-based confidence intervals,’’ J. R. Stat. Soc., Ser. C, 87–94 (1988).

  13. Ch. Z. Mooney, Monte Carlo Simulation (Sage, UK, 1997).

    Book  MATH  Google Scholar 

  14. D. M. Allen, ‘‘Mean square error of prediction as a criterion for selecting variables,’’ Technometrics 13, 469–475 (1971).

    Article  MATH  Google Scholar 

  15. M. Delgado-Rodriguez and J. Llorca, ‘‘Bias,’’ J. Epidemiol. Commun. Health 58, 635–641 (2004).

    Article  Google Scholar 

  16. J. Kyselỳ, ‘‘Coverage probability of bootstrap confidence intervals in heavytailed frequency models, with application to precipitation data,’’ Theor. Appl. Climatol. 101, 345–361 (2010).

    Article  Google Scholar 

  17. S. M. Ross, Simulation (Academic, New York, 2022).

    MATH  Google Scholar 

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Funding

The research of the last listed author was performed under the development program of Volga Region Mathematical Center (agreement no. 075-02-2023-944).

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Correspondence to Nalattaporn Roopmok, Monthira Duangsaphon or Andrei Volodin.

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(Submitted by A. M. Elizarov)

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Roopmok, N., Duangsaphon, M. & Volodin, A. Properties and Parameter Estimation of the Partly-Exponential Distribution. Lobachevskii J Math 44, 3825–3836 (2023). https://doi.org/10.1134/S1995080223090342

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  • DOI: https://doi.org/10.1134/S1995080223090342

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