Mixed Models: Theory and Applications (Wiley Series in Probability and Statistics)

By E. Demidenko. 2004

591 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/e5b03038da09d44bf75505dd7d022d139ad53f23

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Citations in the corpus

The effects of reading material on social and non-social cognition (2015, unknown): “Because fit indices were compared after the addition of each fixed variable, maximum likelihood estimation was used (Demidenko, 2004).”
Improved detection of changes in species richness in high diversity microbial communities (2016, methodology): “r of the richness estimate fullls the role of the standard error of the eect estimate, and the msamples re ect mdierent studies to be pooled. A comprehensive treatment of meta-analyses is given by Demidenko (2004), who discusses both restricted maximum likelihood algorithms and also the best choice ”
Monitoring Correlation Within Linear Profiles Using Mixed Models (2008, methodology): “Goodness-of-fit techniques and other checks of the model assumptions for the LMM, such as those discussed in Verbeke and Molenberghs (2000, Chapter 4) and Demidenko (2004), can be used.”
Actuarial statistics with generalized linear mixed models (2007, methodology): “Full details on linear mixed models can be found in Verbeke & Molenberghs (2000) or Demidenko (2004).”
Rarefaction, Alpha Diversity, and Statistics (2017, methodology): “Modeling parameters observed with estimation error is not a new suggestion: this approach is from the field of statistical meta-analysis, where the results of multiple studies estimating the same effect size is compared (Demidenko, 2004; Willis et al., 2016; Washburne et al., 2018).”
Linear quantile mixed models (2013, background): “This type of distribution is known in a special form in meta-analysis (Demidenko 2004).”
Linear mixed-effects modeling approach to FMRI group analysis (2013, unknown): “…as special cases of LME The linear mixed-effects meta (or multilevel) analysis (MEMA) model (Appendix A) can be treated as a special scenario of the general LME model (Demidenko, 2004; Viechtbauer, 2007) in the sense that the within-subject variance estimate, σ̂ 2i , is available and mi=1.”
Individual-tree diameter growth models for black spruce and jack pine plantations in northern Ontario (2011, methodology): “Observations between sampling units (stands) are independent, but observations within a sampling unit (stand) are dependent because they are from the same sub-population (Demidenko, 2004).”
Balancing Type I Error and Power in Linear Mixed Models (2015, background): “During the last ten years, linear mixed-effects models (LMMs, e.g., Pinheiro & Bates, 2000; Demidenko, 2013) have increasingly replaced mixed-model analyses of variance (ANOVAs) for statistical inference in factorial psycholinguistic experiments.”
Crop rotation and soil temperature influence the community structure of Aspergillus flavus in soil (2010, methodology): “Such mixed models are better suited for experiments with multiple sources of variation including repeated measurements, multiple locations and hierarchical designs (Demidenko, 2004) as used in the present study.”
Height-diameter models with random coefficients and site variables for tree species of Central Maine (2008, background): “by defining a covariance matrix among random parameters within and among sampling levels [4, 6, 10].”