Estimation of One Parameter Exponential Family Under A Precautionary Loss Function Based on Record Values

Li, Jinping; Ren, Haiping

doi:10.5815/ijem.2012.03.11

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…Norstrom (1996) introduced an asymmetric precautionary loss function, which can be defined as follows [7] L θ , θ Based on precautionary loss function, risk function R θ , θ can be derived as follows…”

Section: Bayes Estimator Under Precautionary Loss Functionmentioning

confidence: 99%

Bayesian Estimation for Two Parameters of Gamma Distribution Under Precautionary Loss Function

Naji¹,

Rasheed

2019

IHJPAS

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In the current study, the researchers have been obtained Bayes estimators for the shape and scale parameters of Gamma distribution under the precautionary loss function, assuming the priors, represented by Gamma and Exponential priors for the shape and scale parameters respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared depending on the mean squared errors (MSE’s). The results show that, the performance of Bayes estimator under precautionary loss function with Gamma and Exponential priors is better than other estimates in all cases.

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Section: Bayes Estimator Under Precautionary Loss Functionmentioning

confidence: 99%

Bayesian Estimation for Two Parameters of Gamma Distribution Under Precautionary Loss Function

Naji¹,

Rasheed

2019

IHJPAS

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Bayesian estimation for the reliability function of two parameter exponential distribution under different loss functions

Abd,

Rasheed

2024

International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics: Wmlq2022

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Bayesian and Non-Bayesian Parameters Estimation of Gamma distribution at Various Loss Functions

Rasheed

Naji

2021

J. Phys.: Conf. Ser.

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This paper deals with some Estimator of Bayes s of the parameters of Gamma distribution (GD) under three different loss functions, represented via Precautionary loss function, Entropy loss function (ELF) and invariant of scale squared error (SE) loss function, assuming Gamma and Exponential priors for the shape and scale parameters respectively. Maximum likelihood estimator (MLE) and Lindley’s approximation are used to obtain the Bayes estimates of the shape and scale parameters of GD. According to the Monte Carlo simulation method, those estimators have been compared based on the mean SEs (MSE’s). The results show that the performance of the Estimator of Bayes s under invariant of scale SE loss function produces the best estimates for the two parameters in all cases.

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Estimation of One Parameter Exponential Family Under A Precautionary Loss Function Based on Record Values

Cited by 3 publications

References 5 publications

Bayesian Estimation for Two Parameters of Gamma Distribution Under Precautionary Loss Function

Bayesian Estimation for Two Parameters of Gamma Distribution Under Precautionary Loss Function

Bayesian estimation for the reliability function of two parameter exponential distribution under different loss functions

Bayesian and Non-Bayesian Parameters Estimation of Gamma distribution at Various Loss Functions

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