scholarly journals Bayesian Estimation of the Shape Parameter of Lomax Distribution under Uniform and Jeffery Prior with Engineering Applications

2020 ◽  
Author(s):  
Muhammad IJAZ
Keyword(s):  
Bayesian Estimation ◽  
Shape Parameter ◽  
Engineering Applications ◽  
Lomax Distribution

scholarly journals E-Bayesian and Bayesian Estimation for the Lomax Distribution under Weighted Composite LINEX Loss Function

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Afrah Al-Bossly
Keyword(s):  
Bayesian Estimation ◽  
Loss Function ◽  
Shape Parameter ◽  
Likelihood Estimation ◽  
Loss Functions ◽  
Bayesian Estimator ◽  
Linex Loss Function ◽  
Lomax Distribution ◽  
Linex Loss ◽  
Asymmetric Loss Function

The main contribution of this work is the development of a compound LINEX loss function (CLLF) to estimate the shape parameter of the Lomax distribution (LD). The weights are merged into the CLLF to generate a new loss function called the weighted compound LINEX loss function (WCLLF). Then, the WCLLF is used to estimate the LD shape parameter through Bayesian and expected Bayesian (E-Bayesian) estimation. Subsequently, we discuss six different types of loss functions, including square error loss function (SELF), LINEX loss function (LLF), asymmetric loss function (ASLF), entropy loss function (ENLF), CLLF, and WCLLF. In addition, in order to check the performance of the proposed loss function, the Bayesian estimator of WCLLF and the E-Bayesian estimator of WCLLF are used, by performing Monte Carlo simulations. The Bayesian and expected Bayesian by using the proposed loss function is compared with other methods, including maximum likelihood estimation (MLE) and Bayesian and E-Bayesian estimators under different loss functions. The simulation results show that the Bayes estimator according to WCLLF and the E-Bayesian estimator according to WCLLF proposed in this work have the best performance in estimating the shape parameters based on the least mean averaged squared error.

scholarly journals Bayesian Estimation of Stress-Strength Parameter under Progressive Hybrid Censored Sample in Lomax Distribution

2021 ◽  
Vol 14 (2) ◽  
pp. 0-0
Author(s):  
Akram Kohansal ◽  
Nafiseh Alemohammad ◽  
Fatemeh Azizzadeh ◽  
◽  
◽  
...  
Keyword(s):  
Bayesian Estimation ◽  
Strength Parameter ◽  
Lomax Distribution ◽  
Censored Sample

scholarly journals Bayesian and Maximum Likelihood Estimation of the Shape Parameter of Exponential Inverse Exponential Distribution: A Comparative Approach

2020 ◽  
pp. 28-43
Author(s):  
Innocent Boyle Eraikhuemen ◽  
Fadimatu Bawuro Mohammed ◽  
Ahmed Askira Sule
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Bayesian Estimation ◽  
Exponential Distribution ◽  
Shape Parameter ◽  
Likelihood Estimation ◽  
Monte Carlo Study ◽  
Comparative Approach ◽  
The Mean ◽  
Inverse Exponential Distribution

This paper aims at making Bayesian analysis on the shape parameter of the exponential inverse exponential distribution using informative and non-informative priors. Bayesian estimation was carried out through a Monte Carlo study under 10,000 replications. To assess the effects of the assumed prior distributions and loss function on the Bayesian estimators, the mean square error has been used as a criterion. Overall, simulation results indicate that Bayesian estimation under QLF outperforms the maximum likelihood estimation and Bayesian estimation under alternative loss functions irrespective of the nature of the prior and the sample size. Also, for large sample sizes, all methods perform equally well.

scholarly journals Bayesian estimation of P[Y < X] Based on Record Values from the Lomax Distribution and MCMC Technique

2016 ◽  
Vol 15 (1) ◽  
pp. 488-510 ◽  
Author(s):  
Mohamed A. W Mahmoud ◽  
Rashad M. El-Sagheer ◽  
Ahmed A Soliman ◽  
Ahmed H Abd Ellah
Keyword(s):  
Bayesian Estimation ◽  
Record Values ◽  
Lomax Distribution
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