Efficacy of robust regression applied to fractional factorial treatment structures.

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dc.contributor.author McCants, Michael
dc.date.accessioned 2011-06-20T13:08:14Z
dc.date.available 2011-06-20T13:08:14Z
dc.date.issued 2011-06-20
dc.identifier.uri http://hdl.handle.net/2097/9260
dc.description.abstract Completely random and randomized block designs involving n factors at each of two levels are used to screen for the effects of a large number of factors. With such designs it may not be possible either because of costs or because of time to run each treatment combination more than once. In some cases, only a fraction of all the treatments may be run. With a large number of factors and limited observations, even one outlier can adversely affect the results. Robust regression methods are designed to down-weight the adverse affects of outliers. However, to our knowledge practitioners do not routinely apply robust regression methods in the context of fractional replication of 2^n factorial treatment structures. The purpose of this report is examine how robust regression methods perform in this context. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Robust regression en_US
dc.subject Fractional factorial en_US
dc.title Efficacy of robust regression applied to fractional factorial treatment structures. en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Statistics en_US
dc.description.advisor James J. Higgins en_US
dc.subject.umi Statistics (0463) en_US
dc.date.published 2011 en_US
dc.date.graduationmonth August en_US

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