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Generalized inferences of $$R$$ R = $$\Pr (X>Y)$$ Pr ( X > Y ) for Pareto distribution

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  • Sumith Gunasekera

Abstract

The problem of hypothesis testing and interval estimation based on the generalized variable method of the reliability parameter or the probability $$ R=\Pr (X>Y)$$ R = Pr ( X > Y ) of an item of strength $$X$$ X subject to a stress $$Y$$ Y when $$X$$ X and $$Y$$ Y are independent two-parameter Pareto distributed random variables is given. We discuss the use of p value as a basis for hypothesis testing. There are no exact or approximate testing procedures and confidence intervals for reliability parameter for two-parameter Pareto stress–strength model available in the literature. A simulation study is given to illustrate the proposed generalized variable method. The generalized size, generalized adjusted and unadjusted powers of the test, generalized coverage probabilities are also discussed by comparing with their classical counterparts. Copyright Springer-Verlag Berlin Heidelberg 2015

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  • Sumith Gunasekera, 2015. "Generalized inferences of $$R$$ R = $$\Pr (X>Y)$$ Pr ( X > Y ) for Pareto distribution," Statistical Papers, Springer, vol. 56(2), pages 333-351, May.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:2:p:333-351
    DOI: 10.1007/s00362-014-0584-8
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    References listed on IDEAS

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    Cited by:

    1. Fatih Kızılaslan & Mustafa Nadar, 2018. "Estimation of reliability in a multicomponent stress–strength model based on a bivariate Kumaraswamy distribution," Statistical Papers, Springer, vol. 59(1), pages 307-340, March.

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