Some Bayes estimators for Exponentiated Rayleigh Distribution under Doubly Type II Censored Data

Muslim A. Qasim; Awatif R. Al-Dubaicy

Article Details

Some Bayes estimators for Exponentiated Rayleigh Distribution under Doubly Type II Censored Data

Authors:

Muslim A. Qasim | Awatif R. Al-Dubaicy

Publisher:

Mustansiriyah University

Journal:

Mustansiriyah Journal of Pure and Applied Sciences Vol.1 Issue 1, pp.30-39

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001930644

pISSN:

2957-9910

Keywords:

Abstract:

 Bayesian analysis for the shape parameter  of Exponentiated Rayleigh distribution or (Burr type x) has been considered in this paper, under two priors distributions [Inverted Levy and Quasi] using doubly type II censored data, with two loss functions [Weighted and Precautionary], we conducted a simulation study that evaluate the effectiveness of four estimators by six presumption experiments for varied values of distribution parameters and three sample sizes using mean squared error [MSE]. In general the best estimator of the shape parameter  of the (ERD) using Doubly type II censored data is under Inverted Levy prior under Precautionary loss function in all sample sizes.

Article Details

Some Bayes estimators for Exponentiated Rayleigh Distribution under Doubly Type II Censored Data

Authors: Muslim A. Qasim | Awatif R. Al-Dubaicy

Publisher: Mustansiriyah University

Journal: Mustansiriyah Journal of Pure and Applied Sciences Vol.1 Issue 1, pp.30-39

Document Type: Article

Publication Date: 1/2023

Document Language: English

Document ID (Org No.): 2001930644

pISSN: 2957-9910

Bayesian analysis

Exponentiated Rayleigh distribution

Inverted Levy prior

doubly type II censored data

Issues

Authors:

Muslim A. Qasim | Awatif R. Al-Dubaicy

Publisher:

Mustansiriyah University

Journal:

Mustansiriyah Journal of Pure and Applied Sciences Vol.1 Issue 1, pp.30-39

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001930644

pISSN:

2957-9910