An exploration of the robustness of traditional regression analysis versus analysis using backpropagation networks

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dc.contributor.authorMarkham, Ina Samantaen
dc.contributor.committeechairRakes, Terry R.en
dc.contributor.committeememberHouck, Ernieen
dc.contributor.committeememberMoore, Laurence J.en
dc.contributor.committeememberRees, Loren P.en
dc.contributor.committeememberSumichrast, Robert T.en
dc.contributor.departmentManagement Scienceen
dc.date.accessioned2014-03-14T21:14:09Zen
dc.date.adate2008-06-06en
dc.date.available2014-03-14T21:14:09Zen
dc.date.issued1992en
dc.date.rdate2008-06-06en
dc.date.sdate2008-06-06en
dc.description.abstractResearch linking neural networks and statistics has been at two ends of a spectrum: either highly theoretical or application specific. This research attempts to bridge the gap on the spectrum by exploring the robustness of regression analysis and backpropagation networks in conducting data analysis. Robustness is viewed as the degree to which a technique is insensitive to abnormalities in data sets, such as violations of assumptions. The central focus of regression analysis is the establishment of an equation that describes the relationship between the variables in a data set. This relationship 1s used primarily for the prediction of one variable based on the known values of the other variables. Certain assumptions have to be made regarding the data in order to obtain a tractable solution and the failure of one or more of these assumptions results in poor prediction. The assumptions underlying linear regression that are used to characterize data sets in this research are characterized by: (a) sample size and error variance, (b) outliers, skewness, and kurtosis, (c) multicollinearity, and (d) nonlinearity and underspecification. By using this characterization, the robustness of each technique is studied under what is, in effect, the relaxation of assumptions one at a time. The comparison between regression and backpropagation is made using the root mean square difference between the predicted output from each technique and the actual output.en
dc.description.degreePh. D.en
dc.format.extentxiv, 184 leavesen
dc.format.mediumBTDen
dc.format.mimetypeapplication/pdfen
dc.identifier.otheretd-06062008-170305en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-06062008-170305/en
dc.identifier.urihttp://hdl.handle.net/10919/38395en
dc.language.isoenen
dc.publisherVirginia Techen
dc.relation.haspartLD5655.V856_1992.M375.pdfen
dc.relation.isformatofOCLC# 26112430en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.lccLD5655.V856 1992.M375en
dc.subject.lcshNeural networks (Computer science)en
dc.subject.lcshRegression analysisen
dc.titleAn exploration of the robustness of traditional regression analysis versus analysis using backpropagation networksen
dc.typeDissertationen
dc.type.dcmitypeTexten
thesis.degree.disciplineManagement Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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