Generalized Bayes Estimation Based on a Joint Type-II Censored Sample from K-Exponential Populations

Generalized Bayes is a Bayesian study based on a learning rate parameter. This paper considers a generalized Bayes estimation to study the effect of the learning rate parameter on the estimation results based on a joint censored sample of type-II exponential populations. Squared error, Linex, and general entropy loss functions are used in the Bayesian approach. Monte Carlo simulations were performed to assess how well the different approaches perform. The simulation study compares the Bayesian estimators for different values of the learning rate parameter and different losses.

Automated summary

This paper considers generalized Bayesian estimation based on a learning rate parameter to study the impact of the parameter on the estimation results using a joint censored sample of type-II exponential populations. Squared error, Linex, and general entropy loss functions are used in the Bayesian approach. Monte Carlo simulations are conducted to compare the performance of different approaches for various values of the learning rate parameter and different losses.