Generalized Linear Mixed Models: Modern Concepts, Methods and Applications

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To transform or not to transform: using generalized linear mixed models to analyse reaction time data (2015, unknown): “…interested in more technical mathematical and computational details regarding LMM (Pinheiro and Bates, 2000; Raudenbush and Bryk, 2002; Baayen, 2008), GLM (McCullagh and Nelder, 1989), and GLMM (Jiang, 2007; Stroup, 2013) should consult the excellent resources already published on these topics.”
Statistical guidelines for Apis mellifera research (2013, methodology): “The exemplary data set method (illustrated for GLMMs and in SAS code in Stroup (2013), though easily ported to other software that estimates GLMMs) and use of Monte-Carlo methods (simulation, example explained below, though it is not for a model with random effects) are current recommendations.”
An Assisted Photography Framework to Help Visually Impaired Users Properly Aim a Camera (2014, methodology): “We performed a restricted maximum likelihood (REML) analysis [Patterson and Thompson 1975; Stroup 2012] to evaluate the frequency (out of one) at which this happened on the effects of participant Group (full vision, low vision, and blind), image Target (ground obstacles and schedule sign), and…”
Quilting the Brain: Whole-Brain iEEG Reconstruction via Incomplete Observation Linear Mixed Models (2026, unknown): “Linear Mixed Models (LMM) o(cid:27)er a more (cid:29)exible and statistically rigorous alternative (Stroup, 2016).”