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dc.contributor.advisorHooking, Ronald R.
dc.contributor.advisorWang, Suojin
dc.creatorOu, Shyh-Tyan
dc.description.abstractThe objective of this dissertation is to develop new methods for deriving the confidence intervals of linear functions of variance components. In this dissertation, I propose the parametric bootstrap, saddlepoint and AVE methods to achieve this objective. These proposed methods will be considered in the cases of balanced and unbalanced data. The application of proposed methods on deriving the confidence intervals is described first. Some restrictions of the proposed methods will be discussed. Papers introducing new methods for obtaining confidence intervals generally include a simulation study comparing the coverage probabilities of the proposed intervals with those obtained by existing methods. Most of the time, these papers ignore the discussion for the relative length of the intervals. The length of confidence intervals relative to the magnitude of variance components is of essential importance as it influences the design of the experiment. This dissertation will address the problems. A comparative Monte Carlo study is conducted under the following considerations: (a) Coverage probabilities and average lower bounds. (b) Length relative to magnitude of variance components. (c) The effect of sample sizes and confidence levels.en
dc.format.extentxi, 114 leavesen
dc.rightsThis thesis was part of a retrospective digitization project authorized by the Texas A&M University Libraries. Copyright remains vested with the author(s). It is the user's responsibility to secure permission from the copyright holder(s) for re-use of the work beyond the provision of Fair Use.en
dc.subjectMajor statisticsen
dc.subject.classification1994 Dissertation O93
dc.titleConfidence intervals for variance components in mixed linear modelsen
dc.typeThesisen A&M Universityen of Philosophyen Den
dc.format.digitalOriginreformatted digitalen
dc.publisher.digitalTexas A&M University. Libraries

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