Mixed Models: Theory and Applications with R

By Eugene Demidenko. 2013

368 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bdb9d6e991e3b55860b5d828826b85105335620f

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Citations in the corpus

Multivariate covariance generalized linear models (2015, background): “For a comprehensive description see Demidenko (2013) (Section 4.”
Mixed Effects Models are Sometimes Terrible (2017, methodology): “ed on real-world data sets as well as investigate simulations of imbalanced data to assess how well these models estimate parameters of interest. The general form of a linear mixed model is formally (Demidenko, 2013: 45; Hodges, 2014: 5) defined as: (1) 𝒚𝒚= 𝑿𝑿𝑿𝑿+ 𝒁𝒁𝒁𝒁+ 𝜺𝜺 2 E.g. Rbrul (J”
Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models (2017, unknown): “, Demidenko (2013). Let us now approximate the loglikelihood of Model 2.”
A Guide to Conducting a Meta-Analysis with Non-Independent Effect Sizes (2019, background): “Several authors (e.g., Demidenko, 2013; Riley, 2009) have shown that the estimated fixed effects in a multivariate meta-analysis usually have smaller SEs.”
brms: An R Package for Bayesian Multilevel Models Using Stan (2017, background): “Multilevel models (MLMs) offer a great Ćexibility for researchers across sciences (Brown and Prescott 2015; Demidenko 2013; Gelman and Hill 2006; Pinheiro and Bates 2006).”
Advanced Bayesian Multilevel Modeling with the R Package brms (2017, background): “Multilevel models (MLMs) offer great ﬂexibility for researchers across sciences (Brown and Prescott, 2015; Demidenko, 2013; Gelman and Hill, 2006; Pinheiro and Bates, 2006).”