BAYESIAN ESTIMATION OF THE SHAPE PARAMETER OF GENERALIZED RAYLEIGH DISTRIBUTION UNDER SYMMETRIC AND ASYMMETRIC LOSS FUNCTIONS

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dc.contributor.authorALIYU, YAKUBU
dc.date.accessioned2017-02-13T08:14:54Z
dc.date.available2017-02-13T08:14:54Z
dc.date.issued2016-04
dc.descriptionDISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF A MASTER DEGREE IN STATISTICS DEPARTMENT OF MATHEMATICS, FACULTY OF SCIENCE AHMADU BELLO UNIVERSITY, ZARIA NIGERIAen_US
dc.description.abstractIn 2001, Surles & Padgett introduced Generalized Rayleigh Distribution (GRD). This skewed distribution can be used quiet effectively in modeling life time data. In this work, Bayesian estimates of the shape parameter of a GRD were determined under the assumption of both informative (gamma) and non-informative (Extended Jeffery’s and Uniform) priors. The Bayes estimates were obtained under both symmetric and asymmetric loss functions. The performances of these estimates were compared to the Maximum Likelihood Estimates (MLEs) using Monte Carlo simulation.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/8686
dc.language.isoenen_US
dc.subjectBAYESIAN ESTIMATION,en_US
dc.subjectSHAPE PARAMETER,en_US
dc.subjectGENERALIZED RAYLEIGH DISTRIBUTION,en_US
dc.subjectSYMMETRIC,en_US
dc.subjectASYMMETRIC LOSS FUNCTIONS,en_US
dc.titleBAYESIAN ESTIMATION OF THE SHAPE PARAMETER OF GENERALIZED RAYLEIGH DISTRIBUTION UNDER SYMMETRIC AND ASYMMETRIC LOSS FUNCTIONSen_US
dc.typeThesisen_US
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