Abstract
The purpose of this article offers different algorithms of Weibull Geometric (WG) distribution estimation depending on the progressive Type II censoring samples plan, spatially the joint confidence intervals for the parameters. The approximate joint confidence intervals for the parameters, the approximate confidence regions and percentile bootstrap intervals of confidence are discussed, and several Markov chain Monte Carlo (MCMC) techniques are also presented. The parts of mean square error (MSEs) and credible intervals lengths, the estimators of Bayes depend on non-informative implement more effective than the maximum likelihood estimates (MLEs) and bootstrap. Comparing the models, the MSEs, average confidence interval lengths of the MLEs, and Bayes estimators for parameters are less significant for censored models.
Highlights
The statistical distributions have a very important location of computer branches because of the great number of their particular applications
The purpose of this article offers different algorithms of Weibull Geometric (WG) distribution estimation depending on the progressive Type II censoring samples plan, spatially the joint confidence intervals for the parameters
The parts of mean square error (MSEs) and credible intervals lengths, the estimators of Bayes depend on non-informative implement more effective than the maximum likelihood estimates (MLEs) and bootstrap
Summary
The statistical distributions have a very important location of computer branches because of the great number of their particular applications. El-Sayed et al 102 the integrated Weibull distribution It presents a strong relationship between the brain and the parameters’ values using brain images. MohieEl-Din et al [16] [17] and Elhag et al [18] studied the confidence intervals for parameters of inverse Weibull distribution based on MLE and bootstrap.
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