On testing whether new is better than used using randomly censored data (Q1819518)
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scientific article; zbMATH DE number 3992732
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | On testing whether new is better than used using randomly censored data |
scientific article; zbMATH DE number 3992732 |
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On testing whether new is better than used using randomly censored data (English)
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1987
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Under a model of random censorship, we consider the test \(H_ 0:\) a life distribution is exponential, versus \(H_ 1:\) it is new better than used, but not exponential. This paper introduces a class of tests by using the Kaplan-Meier estimator for the sample distribution in the uncensored model. Under some regularity conditions, the asymptotic normality of statistics is derived by an application of von Mises' method, and asymptotically valid tests are obtained by using estimators for the null standard deviations. The efficiency loss in the proportional censoring model is studied and a Monte Carlo study of power is performed.
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testing exponentiality
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counting process
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von Mises functional
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random censorship
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life distribution
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new better than used
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Kaplan-Meier estimator
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asymptotic normality
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von Mises' method
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asymptotically valid tests
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efficiency loss
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Monte Carlo study
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power
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0.94580746
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0.91696215
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0.8791603
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0.87775695
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0.85747266
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