Mixed Models: Modelling Covariance Structure in the Analysis of Repeated Measures Data

By R. Littell, J. Pendergast, R. Natarajan. 2005

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Seasonal dynamics of above‐ and below‐ground biomass and nitrogen partitioning in Miscanthus × giganteus and Panicum virgatum across three growing seasons (2012, methodology): “The best-fit covariance matrices were chosen for each variable using Akaike’s information criterion to correct for inequality of variance between sampling dates (Keselman et al., 1998; Littell et al., 1998, 2000).”
Foundations of Linear and Generalized Linear Models (2015, methodology): “21 Littell et al. (2000) described a pharmaceutical clinical trial in which 24 patients were randomly assigned to each of three treatment groups (drug A, drug B, placebo) and compared on a measure of respiratory ability (FEV1 = forced expiratory volume in 1 second, in liters).”
Performance of the Kenward–Roger Method when the Covariance Structure is Selected Using AIC and BIC (2005, methodology): “Other resources such as correlograms (Littell et al., 2000), knowledge about the design and science should also be brought into play.”
Individual tree height increment model for managed even-aged stands of ponderosa pine throughout the western United States using linear mixed effects models (2006, result): “Our finding confirms the previous findings (Biging, 1985; Gregoire et al., 1995; Keselman et al., 1999; Hall and Bailey, 2000; Littell et al., 2000; Kowalchuk and Keselman, 2001).”