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Adaptive Nonparametric Distribution-free Procedures In Factorial Data Analysis

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Adaptive Nonparametric Distribution-free Procedures In Factorial Data Analysis

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dc.contributor.author Ferim, Richard Nzagong en_US
dc.date.accessioned 2010-03-03T23:30:44Z
dc.date.available 2010-03-03T23:30:44Z
dc.date.issued 2010-03-03T23:30:44Z
dc.date.submitted January 2009 en_US
dc.identifier.other DISS-10512 en_US
dc.identifier.uri http://hdl.handle.net/10106/2070
dc.description.abstract Many statisticians have questioned the basic assumptions about underlying models which might dominate the analysis of the data in many cases. The assumption of normality without much thought is of concern to a growing group of statisticians. If wrongly assumed, the assumption of normality can lead in serious flaws in the analysis of data. It therefore becomes important to consider distribution-free procedures that don't have to rely on the normality assumption. This is where the adaptive procedures come into play. When data is skewed or light tailed, these adaptive methods produce better results than the regular Wilcoxon and parametric methods. The problem has been solved for a c-sample problem (Sun 1997). Our goal here is to extend this method, to the two-way Anova problem. en_US
dc.description.sponsorship Sun-Mitchell, Shan en_US
dc.language.iso EN en_US
dc.publisher Mathematics en_US
dc.title Adaptive Nonparametric Distribution-free Procedures In Factorial Data Analysis en_US
dc.type Ph.D. en_US
dc.contributor.committeeChair Sun-Mitchell, Shan en_US
dc.degree.department Mathematics en_US
dc.degree.discipline Mathematics en_US
dc.degree.grantor University of Texas at Arlington en_US
dc.degree.level doctoral en_US
dc.degree.name Ph.D. en_US
dc.identifier.externalLink https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=1918
dc.identifier.externalLinkDescription Link to Research Profiles

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