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Applying higher order asymptotics to mixed linear models

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dc.contributor.advisor Peters, Dawn
dc.creator Lyons, Benjamin
dc.date.accessioned 2012-10-01T18:38:20Z
dc.date.available 2012-10-01T18:38:20Z
dc.date.copyright 1997-10-14
dc.date.issued 1997-10-14
dc.identifier.uri http://hdl.handle.net/1957/33989
dc.description Graduation date: 1998 en_US
dc.description.abstract Mixed linear models are a time honored method of analyzing correlated data. However, there is still no method of calculating exact confidence intervals or p-values for an arbitrary parameter in any mixed linear model. Instead, researchers must use either specialized approximate and exact tests that have been developed for particular models or rely on likelihood based approximate tests and confidence intervals which may be unreliable in problems with small sample sizes. This thesis develops procedures to improve small sample likelihood based inference in these important models. The first manuscript develops I.M. Skovgaard's modified directed likelihood for mixed linear models and shows how it is a general, accurate, and easy to apply method of improving inference in mixed linear models. In the second manuscript, O.E. Barndorff-Nielsen's approximate modified profile likelihood is applied to mixed linear models. This modified profile likelihood is a sensible generalization of the commonly used residual likelihood and can be applied if either a fixed or a covariance parameter is of interest. The final manuscript discusses how the design of a mixed linear model effects the accuracy of Skovgaard's modified likelihood and suggests a useful decomposition of that statistic. en_US
dc.language.iso en_US en_US
dc.subject.lcsh Linear models (Statistics) en_US
dc.subject.lcsh Mathematical statistics -- Asymptotic theory en_US
dc.title Applying higher order asymptotics to mixed linear models en_US
dc.type Thesis/Dissertation en_US
dc.degree.name Doctor of Philosophy (Ph. D.) in Statistics en_US
dc.degree.level Doctoral en_US
dc.degree.discipline Science en_US
dc.degree.grantor Oregon State University en_US
dc.description.digitization File scanned at 300 ppi (Monochrome) using Capture Perfect 3.0 on a Canon DR-9050C in PDF format. CVista PdfCompressor 4.0 was used for pdf compression and textual OCR. en_US
dc.description.peerreview no en_us


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