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Full-Text Articles in Physical Sciences and Mathematics
When Should Random Effects Be Included In Estimable Functions And When They Should Not?, David C. Blouin
When Should Random Effects Be Included In Estimable Functions And When They Should Not?, David C. Blouin
Conference on Applied Statistics in Agriculture
In the mixed model, the behavior of linear functions of the fixed and random effects is examined. It is found that inclusion of certain functions of random effects can lead to estimators which are equivalent to those under a fixed effects model and are inconsistent with the inherent structure of the mixed model. Three examples are presented which illustrate the behavior of linear functions of the fixed and random effects. These functions represent the broad, narrow and intermediate inference spaces as introduced by McLean, Sanders and Stroup (1991). Which random effects should be included in the model is discussed. Random …
Analyzing Split-Plot Andrepeated-Measures Designsusing Mixed Models, Russ Wolfinger, Nancy Miles-Mcdermott, Jenny Kendall
Analyzing Split-Plot Andrepeated-Measures Designsusing Mixed Models, Russ Wolfinger, Nancy Miles-Mcdermott, Jenny Kendall
Conference on Applied Statistics in Agriculture
We first introduce the general linear mixed model and provide a justification for using REML to fit it. Then, for an irrigation example, we present several mixed models of increasing complexity. The initial model corresponds to a typical split-plot analysis. Next, we present covariance structures that directly describe the variability of repeated measures within whole plots. Finally, we combine the above types into more complicated mixed models, and compare them using likelihood-based criteria.
Options For Analyzing Unbalanced Split-Plot Experiments: A Case Study, Marta D. Remmenga, Dallas E. Johnson
Options For Analyzing Unbalanced Split-Plot Experiments: A Case Study, Marta D. Remmenga, Dallas E. Johnson
Conference on Applied Statistics in Agriculture
Unbalanced split-plot experiments present many analysis problems. This paper discusses some of the difficulties by comparing the results of the analysis recommended by Milliken and Johnson (1984) to a set of minimal sufficient statistics using a small experiment from Milliken and Johnson as a case study. The estimators used by Milliken and Johnson are not necessarily the best (smallest variance) estimators. A set of minimal sufficient statistics is used to show that the whole plot error term suggested by Milliken and Johnson does not have a distribution that is proportional to an exact chi-square distribution and is not always independent …