Structural Inference on Reliability in a Lognormal Model

dc.contributor.author	Dyer, Danny D.	en
dc.date.accessioned	2010-05-26T18:34:01Z	en
dc.date.available	2010-05-26T18:34:01Z	en
dc.date.issued	1975-12	en
dc.identifier.uri	http://hdl.handle.net/10106/2188	en
dc.description.abstract	Please note that the full text is embargoed 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