Current Issue : October - December Volume : 2013 Issue Number : 4 Articles : 5 Articles
?is work proposes a new methodology for the management of event tree information used in the quantitative risk assessment of\r\ncomplex systems. ?e size of event trees increases exponentially with the number of system components and the number of states\r\nthat each component can be found in. ?eir reduction to a manageable set of events can facilitate risk quanti??cation and safety\r\noptimization tasks. ?e proposed method launches a deductive exploitation of the event space, to generate reduced event trees for\r\nlarge multistate systems. ?e approach consists in the simultaneous treatment of large subsets of the tree, rather than focusing on\r\nthe given single components of the system and getting trapped into guesses on their structural arrangement....
Researchers in reliability engineering regularly encounter variables that are discrete in nature, such as the number of events (e.g.,\r\nfailures) occurring in a certain spatial or temporal interval. ?e methods for analyzing and interpreting such data are o?en based\r\non asymptotic theory, so that when the sample size is not large, their accuracy is suspect. ?is paper discusses statistical inference\r\nfor the reliability of stress-strength models when stress and strength are independent Poisson random variables. ?e maximum\r\nlikelihood estimator and the uniformly minimum variance unbiased estimator are here presented and empirically compared in\r\nterms of their mean square error; recalling the delta method, con??dence intervals based on these point estimators are proposed,\r\nand their reliance is investigated through a simulation study, which assesses their performance in terms of coverage rate and average\r\nlength under several scenarios and for various sample sizes. ?e study indicates that the two estimators possess similar properties,\r\nand the accuracy of these estimators is still satisfactory even when the sample size is small. An application to an engineering\r\nexperiment is also provided to elucidate the use of the proposed methods....
We consider the estimation problem of the probability?? = ??(?? < ??) for Lomax distribution based on general progressive censored\r\ndata. The maximum likelihood estimator and Bayes estimators are obtained using the symmetric and asymmetric balanced loss\r\nfunctions. TheMarkov chainMonte Carlo (MCMC) methods are used to accomplish some complex calculations. Comparisons are\r\nmade between Bayesian and maximum likelihood estimators via Monte Carlo simulation study....
Hoteling�s ??2 control charts are widely used in industries to monitor multivariate processes. The classical estimators, sample\r\nmean, and the sample covariance used in ??2 control charts are highly sensitive to the outliers in the data. In Phase-I monitoring,\r\ncontrol limits are arrived at using historical data after identifying and removing the multivariate outliers. We propose Hoteling�s\r\n??2 control charts with high-breakdown robust estimators based on the reweighted minimum covariance determinant (RMCD)\r\nand the reweighted minimum volume ellipsoid (RMVE) to monitor multivariate observations in Phase-I data. We assessed the\r\nperformance of these robust control charts based on a large number of Monte Carlo simulations by considering different data\r\nscenarios and found that the proposed control charts have better performance compared to existing methods....
We investigate the statistical inferences and applications of the half exponential power distribution for the first time. The proposed\r\nmodel defined on the nonnegative reals extends the half normal distribution and is more flexible. The characterizations and\r\nproperties involving moments and some measures based on moments of this distribution are derived.The inference aspects using\r\nmethods of moment and maximum likelihood are presented. We also study the performance of the estimators using the Monte\r\nCarlo simulation. Finally, we illustrate it with two real applications....
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