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BAYESIAN ANALYSIS OF THE NICOTINE MEASUREMENTS FROM CIGARETTES WITH POWER HAZARD RATE DISTRIBUTION
Keywords:
power hazard rate distribution, uniform and Jeffrey priors, loss functions, applicationDOI:
https://doi.org/10.17654/0972361725044Abstract
In this paper, we construct the Bayes estimators using uniform and Jeffrey priors with different loss functions for the power hazard rate distribution. For numerical illustration, the nicotine measurements have been used to compare various estimators. The findings conclude that Bayes estimator under QELF dominate the performance of other estimators. Further, the Monte Carlo simulation study revealed that the QELF has the smaller values of MSE as compared to others and hence the better estimation may be achieved with QELF for the current data.
- Bayesian estimators have been derived under uniform and Jeffrey priors with four loss functions.
- Under both priors, the MSE of QELF is smaller than other loss functions which conclude that the better estimation can be performed with this function for the current data.
- The results also conclude that WELF rapidly converges to QELF when the sample size is large.
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