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Abstract : Two areas were investigated. A method for producing exact confidence bounds for the reliability of highly reliable series systems. The methods are based on finding suitable orderings of the sample outcome space, and are valid when the probabilities of component failure are small enough for the failure data to be distributed as Poisson random variables. Optimality criteria are invoked to insure that the orderings used produce confidence bounds which are as tight as possible. Tables to facilitate applications were produced. A new class of robust estimators of location were studied. These estimators (called P-estimators) are analogous to Pitman estimators in the same way that M-estimators are analogous to maximum likelihood estimators. Members of this class were evaluated by computer simulation and were found to perform with even higher efficiency than the bisquare M-estimator for both long and short tailed distributions. (Author)
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Abstract : Two areas were investigated. A method for producing exact confidence bounds for the reliability of highly reliable series systems. The methods are based on finding suitable orderings of the sample outcome space, and are valid when the probabilities of component failure are small enough for the failure data to be distributed as Poisson random variables. Optimality criteria are invoked to insure that the orderings used produce confidence bounds which are as tight as possible. Tables to facilitate applications were produced. A new class of robust estimators of location were studied. These estimators (called P-estimators) are analogous to Pitman estimators in the same way that M-estimators are analogous to maximum likelihood estimators. Members of this class were evaluated by computer simulation and were found to perform with even higher efficiency than the bisquare M-estimator for both long and short tailed distributions. (Author)
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