Earticle

초록

영어

Power function distribution is a flexible lifetime distribution that may offer a good fit to some failure data sets. In this paper, we obtain Bayesian estimators of the shape parameter of Power function distribution. For the Posterior distribution of this parameter, we consider Exponential Prior, Pareto Prior, Chi-Square Prior, Quasi Prior and Extension of Jeffrey`s Prior. The three loss functions taken up are Squared Error Loss Function (SELF), Quadratic Loss Function (QLF) and Precautionary Loss Function (PLF). The performance of an estimator is assessed on the basis of its relative Posterior risk. Monte Carlo Simulations are used to compare the performance of the estimators. It is discovered that the PLF produces the least Posterior risk when Exponential and Pareto Priors are used. SELF is the best when Chi-Square, Quasi and Extension of Jeffrey`s Priors are used.

목차

Abstract
 1. Introduction
 2. Posterior Distributions under the Assumption of Different Priors
 3. Bayesian Estimation under Three Loss Functions
  3.1. Squared Error Loss Function (SELF)
  3.2. Quadratic Loss Function (QLF)
  3.3. Precautionary Loss Function (PLF)
 4. Posterior Risks under Different Loss Functions
 5. Simulation Study
 6. Summary and Conclusions
 Acknowledgements
 References

키워드

저자

• Azam Zaka [ Govt. College Ravi Road, Shahdara, Lahore, Pakistan ]
• Ahmad Saeed Akhter [ College of Statistical and Actuarial Sciences, University of the Punjab, Lahore, Pakistan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605