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dc.contributor.advisorPreacher, Kristopher J.
dc.contributor.authorStaggs, Vincent
dc.date.accessioned2009-10-13T02:51:58Z
dc.date.available2009-10-13T02:51:58Z
dc.date.issued2009-04-29
dc.date.submitted2009
dc.identifier.otherhttp://dissertations.umi.com/ku:10355
dc.identifier.urihttp://hdl.handle.net/1808/5513
dc.description.abstractIn mixed models, empirical best linear unbiased estimators of fixed effects generally have mean square errors (MSEs) that cannot be written in closed form. Standard methods of inference depend upon approximation of the estimator MSE, as well as upon approximation of the test statistic distribution by some known distribution, and may not perform well under small samples. The parametric bootstrap interval is presented as an alternative to standard methods of inference. Several parametric bootstrap intervals (Efron percentile, bias-corrected [BC], Hall percentile, and bootstrap-t) were compared using simulated data, along with analytic intervals based on the naïve MSE approximation and the Kenward-Roger method. Among the bootstrap methods, the bootstrap-t seems especially promising.
dc.format.extent73 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.subjectPsychology
dc.subjectPsychometrics
dc.subjectStatistics
dc.subjectBootstrap
dc.subjectHierarchical linear model
dc.subjectMixed model
dc.subjectMultilevel model
dc.titleParametric Bootstrap Interval Approach to Inference for Fixed Effects in the Mixed Linear Model
dc.typeDissertation
dc.contributor.cmtememberDeboeck, Pascal R.
dc.contributor.cmtememberHamilton, Nancy
dc.contributor.cmtememberSkorupski, William P.
dc.contributor.cmtememberWu, Wei
dc.thesis.degreeDisciplinePsychology
dc.thesis.degreeLevelPh.D.
kusw.oastatusna
kusw.oapolicyThis item does not meet KU Open Access policy criteria.
dc.rights.accessrightsopenAccess


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