Bayesian inference on power Lindley distribution based on different loss functions

2019 ◽  
Vol 33 (4) ◽  
pp. 894-914
Author(s):  
Abbas Pak ◽  
M. E. Ghitany ◽  
Mohammad Reza Mahmoudi
Keyword(s):  
Bayesian Inference ◽  
Loss Functions ◽  
Lindley Distribution ◽  
Power Lindley Distribution

Bayesian and non-Bayesian inference under adaptive type-II progressive censored sample with exponentiated power Lindley distribution

2021 ◽  
pp. 1-21
Author(s):  
Hanan Haj Ahmad ◽  
Mukhtar M. Salah ◽  
M. S. Eliwa ◽  
Ziyad Ali Alhussain ◽  
Ehab M. Almetwally ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Type Ii ◽  
Lindley Distribution ◽  
Power Lindley Distribution ◽  
Censored Sample

scholarly journals An Extended Power Lindley Distribution and its Application

2016 ◽  
Vol 11 (2) ◽  
pp. 1075-1094
Author(s):  
Ibrahim Elbatal ◽  
Yehia Mousa El Gebaly ◽  
Essam Ali Amin
Keyword(s):  
Lindley Distribution ◽  
Power Lindley Distribution ◽  
Extended Power

The odd log-logistic Marshall-Olkin power Lindley Distribution: Properties and applications

2017 ◽  
Vol 20 (6) ◽  
pp. 1065-1093 ◽  
Author(s):  
Morad Alizadeh ◽  
S. M. T. K MirMostafaee ◽  
Emrah Altun ◽  
Gamze Ozel ◽  
Maryam Khan Ahmadi
Keyword(s):  
Lindley Distribution ◽  
Power Lindley Distribution

scholarly journals Acceptance Sampling Plans for Finite and Infinite Lot Size under Power Lindley Distribution

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 496
Author(s):  
Saman Shahbaz ◽  
Khushnoor Khan ◽  
Muhammad Shahbaz
Keyword(s):  
Operating Characteristic ◽  
Acceptance Sampling ◽  
Lot Size ◽  
Characteristic Curves ◽  
Sampling Plans ◽  
Lindley Distribution ◽  
Product Life ◽  
Power Lindley Distribution ◽  
Acceptance Sampling Plans ◽  
Lot Sizes

In this paper, we have developed single and double acceptance sampling plans when the product life length follows the power Lindley distribution. The sampling plans have been developed by assuming infinite and finite lot sizes. We have obtained the operating characteristic curves for the resultant sampling plans. The sampling plans have been obtained for various values of the parameters. It has been found that for a finite lot size, the sampling plans provide smaller values of the parameters to achieve the specified acceptance probabilities.

scholarly journals Bayesian Inference of Burr Type VIII Distribution Based on Censored Samples

2014 ◽  
Vol 2014 ◽  
pp. 1-21
Author(s):  
Navid Feroz
Keyword(s):  
Bayesian Inference ◽  
Loss Function ◽  
Simulation Study ◽  
Bayes Estimation ◽  
Loss Functions ◽  
Quadratic Loss ◽  
Bayes Estimators ◽  
Type Viii ◽  
Censored Samples ◽  
Made In

This paper is concerned with estimation of the parameter of Burr type VIII distribution under a Bayesian framework using censored samples. The Bayes estimators and associated risks have been derived under the assumption of five priors and three loss functions. The comparison among the performance of different estimators has been made in terms of posterior risks. A simulation study has been conducted in order to assess and compare the performance of different estimators. The study proposes the use of inverse Levy prior based on quadratic loss function for Bayes estimation of the said parameter.

scholarly journals Exponentiated power Lindley distribution

2015 ◽  
Vol 6 (6) ◽  
pp. 895-905 ◽  
Author(s):  
Samir K. Ashour ◽  
Mahmoud A. Eltehiwy
Keyword(s):  
Lindley Distribution ◽  
Power Lindley Distribution

scholarly journals Estimation of Stress Strength Reliability in Single Component Models for Different Distributions

2019 ◽  
pp. 1-10
Author(s):  
Nafeesa Bashir ◽  
Raeesa Bashir ◽  
T. R. Jan ◽  
Shakeel A. Mir
Keyword(s):  
Likelihood Estimation ◽  
Inverse Power ◽  
Reliability Parameter ◽  
Strength Parameters ◽  
Lindley Distribution ◽  
Power Lindley Distribution ◽  
Method Of Maximum Likelihood ◽  
R Programming ◽  
Component Models ◽  
Inverse Lindley Distribution

This paper aims to estimate the stress-strength reliability parameter R = P(Y < X), considering the two different cases of stress strength parameters, when the strength ‘X’ follows exponentiated inverse power Lindley distribution ,extended inverse Lindley and Stress ‘Y’ follows inverse power Lindley distribution and inverse Lindley distribution. The method of maximum likelihood estimation is used to obtain the reliability estimators. Illustrations are provided using R programming.

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