Parametric Bootstrap Interval Approach to Inference for Fixed Effects in the Mixed Linear Model
Issue Date
2009-04-29Author
Staggs, Vincent
Publisher
University of Kansas
Format
73 pages
Type
Dissertation
Degree Level
Ph.D.
Discipline
Psychology
Rights
This item is protected by copyright and unless otherwise specified the copyright of this thesis/dissertation is held by the author.
Metadata
Show full item recordAbstract
In 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.
Collections
- Dissertations [4626]
- Psychology Dissertations and Theses [459]
Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.