Estimation of Pareto distribution functions from samples contaminated by measurement errors

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dc.contributor.advisorKoen, Chris
dc.contributor.authorKondlo, Lwando Orbet
dc.contributor.otherFaculty of Science
dc.date.accessioned2013-12-04T13:28:54Z
dc.date.accessioned2024-05-14T09:53:16Z
dc.date.available2011/02/16 06:44
dc.date.available2011/02/16
dc.date.available2013-12-04T13:28:54Z
dc.date.available2024-05-14T09:53:16Z
dc.date.issued2010
dc.descriptionMagister Scientiae - MScen_US
dc.description.abstractThe intention is to draw more specific connections between certain deconvolution methods and also to demonstrate the application of the statistical theory of estimation in the presence of measurement error. A parametric methodology for deconvolution when the underlying distribution is of the Pareto form is developed. Maximum likelihood estimation (MLE) of the parameters of the convolved distributions is considered. Standard errors of the estimated parameters are calculated from the inverse Fisher’s information matrix and a jackknife method. Probability-probability (P-P) plots and Kolmogorov-Smirnov (K-S) goodnessof- fit tests are used to evaluate the fit of the posited distribution. A bootstrapping method is used to calculate the critical values of the K-S test statistic, which are not available.en_US
dc.description.countrySouth Africa
dc.identifier.urihttps://hdl.handle.net/10566/14915
dc.language.isoenen_US
dc.publisherUniversity of the Western Capeen_US
dc.rights.holderUniversity of the Western Capeen_US
dc.subjectDeconvolutionen_US
dc.subjectDistribution functionsen_US
dc.subjectError-Contaminated samplesen_US
dc.subjectErrors-in-variablesen_US
dc.subjectJackknifeen_US
dc.subjectMaximum likelihood methoden_US
dc.subjectMeasurement errorsen_US
dc.subjectNonparametric estimationen_US
dc.subjectPareto distributionen_US
dc.titleEstimation of Pareto distribution functions from samples contaminated by measurement errorsen_US
dc.typeThesisen_US

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