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dc.contributor.advisorWoods, Carol M
dc.contributor.authorHarpole, Jared Kenneth
dc.date.accessioned2013-08-24T22:34:59Z
dc.date.available2013-08-24T22:34:59Z
dc.date.issued2013-05-31
dc.date.submitted2013
dc.identifier.otherhttp://dissertations.umi.com/ku:12636
dc.identifier.urihttp://hdl.handle.net/1808/11725
dc.description.abstractExploratory data analysis (EDA) is important, yet often overlooked in the social and behavioral sciences. Graphical analysis of one's data is central to EDA. A viable method of estimating and graphing the underlying density in EDA is kernel density estimation (KDE). A problem with using KDE involves correctly specifying the bandwidth to portray an accurate representation of the density. The purpose of the present study is to empirically evaluate how the choice of bandwidth in KDE influences recovery of the true density. Simulations were carried out that compared five bandwidth selection methods [Sheather-Jones plug-in (SJDP), Normal rule of thumb (NROT), Silverman's rule of thumb (SROT), Least squares cross-validation (LSCV), and Biased cross-validation (BCV)], using four true density shapes (Standard Normal, Positively Skewed, Bimodal, and Skewed Bimodal), and eight sample sizes (25, 50, 75, 100, 250, 500, 1000, 2000). Results indicated that overall SJDP performed best. However, this was specifically true for samples between 250 and 2,000. For smaller samples (N = 25 to 100), SROT performed best. Thus, either the SJDP or SROT is recommended depending on the sample size.
dc.format.extent48 pages
dc.language.isoen
dc.publisherUniversity of Kansas
dc.rightsThis item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
dc.subjectQuantitative psychology
dc.subjectPsychometrics
dc.subjectStatistics
dc.subjectBandwidth selection
dc.subjectExploratory data analysis
dc.subjectGraphical analysis
dc.subjectKernel density estimation
dc.titleHow Bandwidth Selection Algorithms Impact Exploratory Data Analysis Using Kernel Density Estimation
dc.typeThesis
dc.contributor.cmtememberDeboeck, Pascal R.
dc.contributor.cmtememberJohnson, Paul
dc.thesis.degreeDisciplinePsychology
dc.thesis.degreeLevelM.A.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
kusw.bibid8086241
dc.rights.accessrightsopenAccess


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