Research Article

STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION

Volume: 37 Number: 3 September 1, 2020
  • İlhan Usta

STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION

Abstract

This paper deals with the statistical inference for the geometric process (GP), in which the time until the occurrence of the first event is assumed to follow inverse Rayleigh distribution. The maximum likelihood (ML) method is used to derive the estimators of the parameters in GP. Asymptotic distributions of the ML estimators are obtained which help us to construct confidence intervals for the parameters and show the consistency of these estimators. The performances of the ML estimators are also compared with the corresponding non-parametric modified moment estimators in terms of bias, mean squared error and Pitman nearness probability through an extensive simulation study. Finally, a real data set is provided to illustrate the results.

Keywords

References

  1. [1] Cox D.R., Lewis P.A.W., (1966) The Statistical Analysis of Series of Events. Mathuen, London.
  2. [2] Ascher H., Feingold H., (1984) Repairable Systems Reliability. Marcel Dekker, New York.
  3. [3] Lam Y., (1988) A Note on the optimal replacement problem, Advances in Applied Probability 20, 479–482.
  4. [4] Lam Y., (1988) Geometric process and replacement problem, Acta Mathematicae Applicatae Sinica 4, 366–377.
  5. [5] Lam Y., (1992) Nonparametric inference for geometric process, Communications in Statistics – Theory and Methods 21, 2083– 2105.
  6. [6] Lam Y., Zhu L.X., Chan J.S.K., Liu Q., (2004) Analysis of data from a series of events by a geometric process model, Acta Mathematicae Applicatae Sinica 20, 263–282.
  7. [7] Lam Y., (2007) The Geometric Process and Its Applications. World Scientific, Singapore.
  8. [8] Lam Y., Zheng Y.H., Zhang Y.L., (2003) Some limit theorems in geometric process, Acta Mathematicae Applicatae Sinica 19, 405–416.

Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Publication Date

September 1, 2020

Submission Date

March 13, 2019

Acceptance Date

May 3, 2019

Published in Issue

Year 2019 Volume: 37 Number: 3

APA
Usta, İ. (2020). STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION. Sigma Journal of Engineering and Natural Sciences, 37(3), 871-882. https://izlik.org/JA73ZP59WL
AMA
1.Usta İ. STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION. SIGMA. 2020;37(3):871-882. https://izlik.org/JA73ZP59WL
Chicago
Usta, İlhan. 2020. “STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION”. Sigma Journal of Engineering and Natural Sciences 37 (3): 871-82. https://izlik.org/JA73ZP59WL.
EndNote
Usta İ (September 1, 2020) STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION. Sigma Journal of Engineering and Natural Sciences 37 3 871–882.
IEEE
[1]İ. Usta, “STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION”, SIGMA, vol. 37, no. 3, pp. 871–882, Sept. 2020, [Online]. Available: https://izlik.org/JA73ZP59WL
ISNAD
Usta, İlhan. “STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION”. Sigma Journal of Engineering and Natural Sciences 37/3 (September 1, 2020): 871-882. https://izlik.org/JA73ZP59WL.
JAMA
1.Usta İ. STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION. SIGMA. 2020;37:871–882.
MLA
Usta, İlhan. “STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION”. Sigma Journal of Engineering and Natural Sciences, vol. 37, no. 3, Sept. 2020, pp. 871-82, https://izlik.org/JA73ZP59WL.
Vancouver
1.İlhan Usta. STATISTICAL INFERENCE FOR GEOMETRIC PROCESS WITH THE INVERSE RAYLEIGH DISTRIBUTION. SIGMA [Internet]. 2020 Sep. 1;37(3):871-82. Available from: https://izlik.org/JA73ZP59WL

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