SELECTING THE COVARIANCE STRUCTURE IN MIXED MODEL USING STATISTICAL METHODS CALIBRATION
- 1 King Abdul Aziz University, Saudi Arabia
Abstract
In this article the analysis of experiment of repeated measures design is considered which is used often in different fields of studies. In order to analyze the experiment of repeated measures design efficiently we need to select the suitable covariance structure which required a lot of efforts. In the current paper an approach is used to guide the selection of the covariance structure for the analysis of repeated measures design with high rate of success. Five well known model selection criteria are used in the approach. Simulation study is used to evaluate the approach in terms of its ability to select the right covariance structure. The evaluation of the approach was in terms of its percentage of times that it identifies the right covariance structure. Overall, the simulation study showed excellent performance for the approach in all the study cases. The main result of our article is that we recommend considering the approach as a standard way to select the right covariance structure.
DOI: https://doi.org/10.3844/jmssp.2014.309.315
Copyright: © 2014 Ali Hussein AL-Marshadi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- Repeated Measures Design
- Information Criteria
- Bootstrap Procedure
- Hierarchical Clustering Methods
- Single Linkage Distance Measure
- Kenward-Roger Method
- Restricted Maximum Likelihood (REML)