Skip to main content
Log in

E-Bayesian and Hierarchical Bayesian Estimation for the Shape Parameter and Reversed Hazard Rate of Power Function Distribution Under Different Loss Functions

  • Research article
  • Published:
Journal of the Indian Society for Probability and Statistics Aims and scope Submit manuscript

Abstract

This paper presents the E-Bayesian and hierarchical Bayesian estimation of the shape parameter and reversed hazard rate of Power function distribution under the condition that scale parameter is known, based on the different loss functions. We also study some important properties of the proposed estimators. Based on Monte Carlo Simulation study and using a real dataset comparisons are made between the proposed estimators and existing Bayesian estimators. Confidence intervals and coverage probability of the estimators are also computed. From the numerical studies it can be seen that the proposed E-Bayesian and hierarchical Bayesian estimators have better performance when compared to the existing Bayesian estimators in terms of bias and MSE respectively.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abdul-Sathar E, Renjini K, Rajesh G, Jeevanand E (2015) Bayes estimation of lorenz curve and gini-index for power function distribution. S Afr Stat J 49(1):21–33

    MathSciNet  MATH  Google Scholar 

  • Ali MM, Woo J, Nadarajah S (2005) On the ratic x/(x+ y) for the power function distribution. Pak J Stat All Ser 21(2):131

    MATH  Google Scholar 

  • Ando T, Zellner A et al (2010) Hierarchical bayesian analysis of the seemingly unrelated regression and simultaneous equations models using a combination of direct monte carlo and importance sampling techniques. Bayesian Anal 5(1):65–95

    Article  MathSciNet  Google Scholar 

  • Arslan G (2014) A new characterization of the power distribution. J Comput Appl Math 260:99–102

    Article  MathSciNet  Google Scholar 

  • Bagchi S, Sarkar P (1986) Bayes interval estimation for the shape parameter of the power distribution. IEEE Trans Reliab 35(4):396–398

    Article  Google Scholar 

  • Belzunce F, Candel J, Ruiz J (1998) Ordering and asymptotic properties of residual income distributions. Sankhyā Indian J Stat Ser B 60:331–348

    MathSciNet  MATH  Google Scholar 

  • Berger JO (1985) Statistical decision theory and Bayesian analysis. Springer, Berlin

    Book  Google Scholar 

  • Childs A, Balakrishnan N (2002) Conditional inference procedures for the pareto and power function distributions based on type-II right censored samples. Statistics 36(3):247–257

    Article  MathSciNet  Google Scholar 

  • Han M (1997) The structure of hierarchical prior distribution and its applications. Chin Oper Res Manag Sci 6(3):31–40

    MathSciNet  Google Scholar 

  • Han M (2009) E-Bayesian estimation and hierarchical bayesian estimation of failure rate. Appl Math Model 33(4):1915–1922

    Article  MathSciNet  Google Scholar 

  • Han M (2011) E-Bayesian estimation of the reliability derived from binomial distribution. Appl Math Model 35(5):2419–2424

    Article  MathSciNet  Google Scholar 

  • Han M (2017) The E-Bayesian and hierarchical Bayesian estimations of pareto distribution parameter under different loss functions. J Stat Comput Simul 87(3):577–593

    Article  MathSciNet  Google Scholar 

  • Han M, Ding Y (2004) Synthesized expected Bayesian method of parametric estimate. J Syst Sci Syst Eng 13(1):98–111

    Article  Google Scholar 

  • Huss M, Holme P (2007) Currency and commodity metabolites: their identification and relation to the modularity of metabolic networks. IET Syst Biol 1(5):280–285

    Article  Google Scholar 

  • Jaheen ZF, Okasha HM (2011) E-Bayesian estimation for the burr type XII model based on type-2 censoring. Appl Math Model 35(10):4730–4737

    Article  MathSciNet  Google Scholar 

  • Khan M, Islam H (2007) On stress and strength having power function distributions. Pak J Stat All Ser 23(1):83

    MathSciNet  MATH  Google Scholar 

  • Lindley DV, Smith AF (1972) Bayes estimates for the linear model. J R Stat Soc Ser B (Methodol) 34:1–41

    MathSciNet  MATH  Google Scholar 

  • Lutful Kabir A, Ahsanullah M (1974) Estimation of the location and scale parameters of a power-function distribution by linear functions of order statistics. Commun Stat Theory Methods 3(5):463–467

    MathSciNet  MATH  Google Scholar 

  • Meniconi M, Barry D (1996) The power function distribution: a useful and simple distribution to assess electrical component reliability. Microelectron Reliab 36(9):1207–1212

    Article  Google Scholar 

  • Osei FB, Duker AA, Stein A (2011) Hierarchical Bayesian modeling of the space–time diffusion patterns of cholera epidemic in Kumasi, Ghana. Stat Neerl 65(1):84–100

    Article  MathSciNet  Google Scholar 

  • Rahman H, Roy M, Baizid AR (2012) Bayes estimation under conjugate prior for the case of power function distribution. Am J Math Stat 2(3):44–48

    Article  Google Scholar 

  • Saleem M, Aslam M, Economou P (2010) On the Bayesian analysis of the mixture of power function distribution using the complete and the censored sample. J Appl Stat 37(1):25–40

    Article  MathSciNet  Google Scholar 

  • Tavangar M (2011) Power function distribution characterized by dual generalized order statistics. J Iran Stat Soc 10(1):13–27

    MathSciNet  MATH  Google Scholar 

  • Zaka A, Akhter AS (2014) Bayesian analysis of power function distribution using different loss functions. Int J Hybrid Inf Technol 7(6):229–244

    Article  Google Scholar 

  • Zarrin S, Saxena S, Kamal M (2013) Reliability computation and bayesian analysis of system reliability of power function distribution. Int J Adv Eng Sci Technol 2(4):76–86

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the Editor in Chief and the reviewers for their constructive comments which helped to improve the quality of the present paper to a great extend.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to E. I. Abdul-Sathar.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdul-Sathar, E.I., Krishnan, R.B.A. E-Bayesian and Hierarchical Bayesian Estimation for the Shape Parameter and Reversed Hazard Rate of Power Function Distribution Under Different Loss Functions. J Indian Soc Probab Stat 20, 227–253 (2019). https://doi.org/10.1007/s41096-019-00069-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41096-019-00069-4

Keywords

Navigation