Bayesian and Non-Bayesian Estimation Using Balanced Loss Functions

1994 ◽  
pp. 377-390 ◽  
Author(s):  
Arnold Zellner
Keyword(s):  
Bayesian Estimation ◽  
Loss Functions ◽  
Balanced Loss Functions

scholarly journals Bayesian Estimation of the Parameter of the p-Dimensional Size-Biased Rayleigh Distribution

2017 ◽  
Vol 56 (1) ◽  
pp. 88-91
Author(s):  
Arun Kumar Rao ◽  
Himanshu Pandey ◽  
Kusum Lata Singh
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Bayesian Estimation ◽  
Density Function ◽  
Rayleigh Distribution ◽  
Survival Model ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Hazard Functions ◽  
Squared Error

In this paper, we have derived the probability density function of the size-biased p-dimensional Rayleigh distribution and studied its properties. Its suitability as a survival model has been discussed by obtaining its survival and hazard functions. We also discussed Bayesian estimation of the parameter of the size-biased p-dimensional Rayleigh distribution. Bayes estimators have been obtained by taking quasi-prior. The loss functions used are squared error and precautionary.

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

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.

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