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dc.contributor.authorKoekemoer, Gerhard
dc.contributor.authorSwanepoel, Jan W.H.
dc.date.accessioned2010-05-26T09:47:38Z
dc.date.available2010-05-26T09:47:38Z
dc.date.issued2008
dc.identifier.citationKoekemoer, G. & Swanepoel, J.W.H. 2008. A semi-parametric method for transforming data to normality. Statistics and computing, 18(3):241-257. [https://doi.org/10.1007/s11222-008-9053-3]en
dc.identifier.issn0960-3174
dc.identifier.issn1573-1375 (Online)
dc.identifier.urihttp://hdl.handle.net/10394/3085
dc.identifier.urihttps://doi.org/10.1007/s11222-008-9053-3
dc.identifier.urihttps://link.springer.com/article/10.1007%2Fs11222-008-9053-3
dc.description.abstractA non-parametric transformation function is introduced to transform data to any continuous distribution. When transformation of data to normality is desired, the use of a suitable parametric pre-transformation function improves the performance of the proposed non-parametric transformation function. The resulting semi-parametric transformation function is shown empirically, via a Monte Carlo study, to perform at least as well as any parametric transformation currently available in the literature.
dc.language.isoenen
dc.publisherSpringer
dc.subjectNormality
dc.subjectProfile likelihood
dc.subjectqq-Plots
dc.subjectSemi-parametric
dc.subjectTransformation
dc.titleA semi-parametric method for transforming data to normalityen
dc.typeArticleen
dc.contributor.researchID10177507 - Swanepoel, Jan Willem Hendrik
dc.contributor.researchID10096353 - Koekemoer, Gerhard


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