Algorithms of Confidence Intervals of WG Distribution Based on Progressive Type-II Censoring Samples

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.

Remaining Useful Life Prognosis of Aircraft Brakes

“…We train those B ANNs using as a cost function the mean squared error. Confidence Intervals can be constructed if we realize a large number of ANNs (B>100) and assume that the predictions follow a normal distribution as utilized in (Khosravi, Nahavandi & Atiya, 2011), (Pierce, Worden & Bezazi, 2008), (El-Sayed, Riad, Elsafty & Estaitia, 2017). The mean prediction values and the variance can then be simply calculated as: can be constructed as:…”