Structural Inference on Reliability in a Lognormal Model

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Structural Inference on Reliability in a Lognormal Model

Show simple item record Dyer, Danny D. en 2010-05-26T18:34:01Z en 2010-05-26T18:34:01Z en 1975-12 en
dc.identifier.uri en
dc.description.abstract The theory of structural inference, as developed by Fraser (1968), is based on a group-theoretic approach using invariant Haar measures to Fisher's fiducial theory. Structural inference theory constructs a unique distribution, conditional on the given sample information only, for the parameters of a measurement model. Based on the structural density for the two-parameter lognormal distribution, the structural density and distribution function for the reliability function are derived. Consequently, expressions for structural point and interval estimates of the reliability function are developed. Approximations for large sample sizes and/or moderately reliable components are also discussed. An example based on lognormal data is given to illustrate the theory. en
dc.language.iso en_US en
dc.publisher University of Texas at Arlington en
dc.relation.ispartofseries Technical Report;35 en
dc.subject Structural inference theory en
dc.subject Fisher's fiducial theory en
dc.subject Haar measure en
dc.subject Lognormal data en
dc.subject.lcsh Mathematics Research en
dc.title Structural Inference on Reliability in a Lognormal Model en
dc.type Technical Report en
dc.publisher.department Department of Mathematics en

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