scholarly journals Estimation of Scale Parameter of Weibull-Lomax distribution via Bayesian Approach

2021 ◽  
Vol 09 (05) ◽  
Author(s):  
Arun Kumar Rao, ◽  
Keyword(s):  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Scale Parameter ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Lomax Distribution ◽  
Squared Error

In this paper, the Weibull-Lomax 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 Parameter Estimation of Length Biased Weighted Frechet Distribution via Bayesian Approach

2021 ◽  
pp. 42-51
Author(s):  
Arun Kumar Rao ◽  
Himanshu Pandey
Keyword(s):  
Parameter Estimation ◽  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Fréchet Distribution ◽  
Squared Error ◽  
Frechet Distribution

In this paper, length-biased weighted Frechet 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 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 Bayes Estimation of Shape Parameter of Length Biased Weibull Distribution

2021 ◽  
Vol 5 (1) ◽  
pp. 28
Author(s):  
Arun Kumar Rao ◽  
Himanshu Pandey
Keyword(s):  
Weibull Distribution ◽  
Bayesian Analysis ◽  
Shape Parameter ◽  
Bayes Estimation ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Squared Error

In this paper, length biased Weibull 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 Bayes approach to study scale parameter of log logistic distribution

2015 ◽  
Vol 3 (2) ◽  
pp. 185
Author(s):  
Wajiha Nasir ◽  
Maria Maria ◽  
Muhammad Aslam
Keyword(s):  
Numerical Integration ◽  
Bayesian Approach ◽  
Simulation Study ◽  
Posterior Distribution ◽  
Scale Parameter ◽  
Loss Functions ◽  
Logistic Distribution ◽  
Bayes Estimators ◽  
Informative Prior ◽  
Close Form

<p>Scale parameter of Log logistic distribution has been studied using Bayesian approach. Posterior distribution has derived by using non informative prior. Posterior distribution is not in close form so we have work with quadrature numerical integration. Various loss functions has been utilized to derive the Bayes estimators and their corresponding risks. Simulation study has been performed to compare the performance of different estimators.</p>

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.

scholarly journals A Bayesian Approach for Estimating Parameter of Rayleigh Distribution

2019 ◽  
Vol 11 (1) ◽  
pp. 23-39
Author(s):  
J. Mahanta ◽  
M. B. A. Talukdar
Keyword(s):  
Weibull Distribution ◽  
Loss Function ◽  
Bayesian Approach ◽  
Rayleigh Distribution ◽  
Loss Functions ◽  
Bayes Risk ◽  
Squared Error ◽  
Different Types ◽  
Two Parameters ◽  
Special Case

This paper is concerned with estimating the parameter of Rayleigh distribution (special case of two parameters Weibull distribution) by adopting Bayesian approach under squared error (SE), LINEX, MLINEX loss function. The performances of the obtained estimators for different types of loss functions are then compared. Better result is found in Bayesian approach under MLINEX loss function. Bayes risk of the estimators are also computed and presented in graphs.

Data Processing with Computation in Bayes Reliability Analysis for Burr Type X Distribution under Different Loss Functions

2014 ◽  
Vol 978 ◽  
pp. 205-208
Author(s):  
Hui Zhou
Keyword(s):  
Prior Distribution ◽  
Unknown Parameter ◽  
Empirical Bayes ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Likelihood Estimator ◽  
Squared Error Loss ◽  
Squared Error ◽  
Linex Loss ◽  
Empirical Bayes Estimators

This paper studies the estimation of the parameter of Burr Type X distribution. Maximum likelihood estimator is first derived, and then the Bayes and Empirical Bayes estimators of the unknown parameter are obtained under three loss functions, which are squared error loss, LINEX loss and entropy loss functions. The prior distribution of parmeter used in this paper is Gamma distribution. Finally, a Monte Carlo simulation is given to illustrate the application of these estimators.

scholarly journals Classical and Bayesian Approach in Estimation of Scale Parameter of Nakagami Distribution

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Kaisar Ahmad ◽  
S. P. Ahmad ◽  
A. Ahmed
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimator ◽  
Bayesian Approach ◽  
Simulation Study ◽  
Bayesian Method ◽  
Scale Parameter ◽  
Loss Functions ◽  
Likelihood Estimator ◽  
R Software ◽  
Nakagami Distribution

Nakagami distribution is considered. The classical maximum likelihood estimator has been obtained. Bayesian method of estimation is employed in order to estimate the scale parameter of Nakagami distribution by using Jeffreys’, Extension of Jeffreys’, and Quasi priors under three different loss functions. Also the simulation study is conducted in R software.

scholarly journals Bayesian Estimation of the Scale Parameter of Inverse Weibull Distribution under the Asymmetric Loss Functions

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Farhad Yahgmaei ◽  
Manoochehr Babanezhad ◽  
Omid S. Moghadam
Keyword(s):  
Weibull Distribution ◽  
Scale Parameter ◽  
Loss Functions ◽  
Simulation Studies ◽  
Bayes Estimators ◽  
Likelihood Estimator ◽  
Risk Functions ◽  
Inverse Weibull Distribution ◽  
The Mean ◽  
Mean Square Errors

This paper proposes different methods of estimating the scale parameter in the inverse Weibull distribution (IWD). Specifically, the maximum likelihood estimator of the scale parameter in IWD is introduced. We then derived the Bayes estimators for the scale parameter in IWD by considering quasi, gamma, and uniform priors distributions under the square error, entropy, and precautionary loss functions. Finally, the different proposed estimators have been compared by the extensive simulation studies in corresponding the mean square errors and the evolution of risk functions.

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