Return to search

A study of covariance structure selection for split-plot designs analyzed using mixed models

Master of Science / Department of Statistics / Christopher I. Vahl / In the classic split-plot design where whole plots have a completely randomized design, the conventional analysis approach assumes a compound symmetry (CS) covariance structure for the errors of observation. However, often this assumption may not be true. In this report, we examine using different covariance models in PROC MIXED in the SAS system, which are widely used in the repeated measures analysis, to model the covariance structure in the split-plot data in which the simple compound symmetry assumption does not hold. The comparison of the covariance structure models in PROC MIXED and the conventional split-plot model is illustrated through a simulation study. In the example analyzed, the heterogeneous compound symmetry (CSH) covariance model has the smallest values for the Akaike and Schwarz’s Bayesian information criteria fit statistics and is therefore the best model to fit our example data.

Identiferoai:union.ndltd.org:KSU/oai:krex.k-state.edu:2097/18129
Date January 1900
CreatorsQiu, Chen
PublisherKansas State University
Source SetsK-State Research Exchange
Languageen_US
Detected LanguageEnglish
TypeReport

Page generated in 0.0019 seconds