A BAYESIAN ANALYSIS OF RECORD STATISTICS FROM THE LOMAX MODEL UNDER BALANCED LOSS FUNCTIONS WITH NONLINEAR PROGRAMMING

2021 ◽  
Vol 67 (1) ◽  
pp. 33-45
Author(s):  
Fuad S. Alduais
Keyword(s):  
Nonlinear Programming ◽  
Bayesian Analysis ◽  
Loss Functions ◽  
Record Statistics ◽  
Balanced Loss Functions

Bayesian and Robust Bayesian analysis under a general class of balanced loss functions

2010 ◽  
Vol 53 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Mohammad Jafari Jozani ◽  
Éric Marchand ◽  
Ahmad Parsian
Keyword(s):  
Bayesian Analysis ◽  
General Class ◽  
Loss Functions ◽  
Robust Bayesian Analysis ◽  
Balanced Loss Functions

scholarly journals Nonlinear Programming to Determine Best Weighted Coefficient of Balanced LINEX Loss Function Based on Lower Record Values

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Fuad S. Al-Duais ◽  
Mohammed Alhagyan
Keyword(s):  
Nonlinear Programming ◽  
Loss Function ◽  
Record Values ◽  
Rayleigh Distribution ◽  
Loss Functions ◽  
Balanced Loss Function ◽  
Lower Record ◽  
Lower Record Values ◽  
Mean Square Errors ◽  
Balanced Loss Functions

Majority research studies in the literature determine the weighted coefficients of balanced loss function by suggesting some arbitrary values and then conducting comparison study to choose the best. However, this methodology is not efficient because there is no guarantee ensures that one of the chosen values is the best. This encouraged us to look for mathematical method that gives and guarantees the best values of the weighted coefficients. The proposed methodology in this research is to employ the nonlinear programming in determining the weighted coefficients of balanced loss function instead of the unguaranteed old methods. In this research, we consider two balanced loss functions including balanced square error (BSE) loss function and balanced linear exponential (BLINEX) loss function to estimate the parameter and reliability function of inverse Rayleigh distribution (IRD) based on lower record values. Comparisons are made between Bayesian estimators (SE, BSE, LINEX, and BLINEX) and maximum likelihood estimator via Monte Carlo simulation. The evaluation was done based on absolute bias and mean square errors. The outputs of the simulation showed that the balanced linear exponential (BLINEX) loss function has the best performance. Moreover, the simulation verified that the balanced loss functions are always better than corresponding loss function.

Estimation of a normal mean relative to balanced loss functions

2004 ◽  
Vol 45 (2) ◽  
pp. 279-286 ◽  
Author(s):  
N. Sanjari Farsipour ◽  
A. Asgharzadeh
Keyword(s):  
Loss Functions ◽  
Normal Mean ◽  
Balanced Loss Functions

scholarly journals Bayesian Estimation of Exponentiated Inverse Rayleigh Distribution

2021 ◽  
Vol 9 (03) ◽  
pp. 321-328
Author(s):  
Arun Kumar Rao ◽  
Himanshu Pandey
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Estimation ◽  
Rayleigh Distribution ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Squared Error

In this paper, exponentiated inverse Rayleigh distribution is considered for Bayesian analysis. The expressions for Bayes estimators of the parameter have been derived under squared error, precautionary, entropy, K-loss, and Al-Bayyati’s loss functions by using quasi and gamma priors.

scholarly journals A Bayesian analysis of record statistics from the Rayleigh model

2007 ◽  
Vol 2 ◽  
pp. 619-631 ◽  
Author(s):  
M. I. Hendi ◽  
S. E. Abu-Youssef ◽  
A. A. Alraddadi
Keyword(s):  
Bayesian Analysis ◽  
Record Statistics

scholarly journals On shrinkage estimation for balanced loss functions

2020 ◽  
Vol 175 ◽  
pp. 104558 ◽  
Author(s):  
Éric Marchand ◽  
William E. Strawderman
Keyword(s):  
Loss Functions ◽  
Shrinkage Estimation ◽  
Balanced Loss Functions
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