Mixed effects models

By M. Loon. 2013

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Citations in the corpus

Deep Factors for Forecasting (2019, background): “Mixed effect models (Crawley, 2012) consist of two kinds of effects: ﬁxed (global) effects that describe the whole population, and random (local) effects that capture the id-iosyncratic of individuals or subgroups.”
Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020, background): “Examples include mixed effect models [40], which consist of two kinds of effects: fixed (global) effects that describe the whole population, and random (local) effects that capture the idiosyncratic of individuals or subgroups.”
Wearable Sleep Technology in Clinical and Research Settings (2019, background): “The latter issues can be overcome using mixedeffects models which can account for both the average population behavior and the natural heterogeneity of participant outcomes (66).”
Dialect-robust Evaluation of Generated Text (2022, methodology): “…this with two styles of analysis: Mixed-effect Regression For metric m, example i, and condition j ∈ {perturb, dialect, MT}, we model the metric σ(j)m,i via a mixed-effects regression (Baayen, 2012; Speelman et al., 2018), σ (j) i = θi + φj + i,j , (7) with the subscriptm implicit in each term.”
Rapid ecological replacement of a native bumble bee by invasive species. (2013, methodology): “The value of the ACF at different time intervals gives an indication of whether there is any autocorrelation in the data (Zuur et al. 2009).”
A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, methodology): “…using compound probability distributions such as the Negative-Binomial for count data (Hilbe 2011; Harrison 2014) or Beta-Binomial for proportion data (Harrison 2015), and so it is good practice to assess the relative fit of both types of model using AIC before proceeding (e.g. Zuur et al 2009).”
Fire interval effects on persistence of resprouter species in Mediterranean-type shrublands (2011, methodology): “To maintain model parsimony, 205 covariates were removed from the model if Anova-based comparison of maximum likelihood 206 values were not significant (Zuur et al. 2009).”
Predicting pauses in L1 and L2 speech: the effects of utterance boundaries and word frequency (2016, unknown): “Both random effects were highly significant, as shown by likelihood ratio tests (Pinheiro and Bates 2000).”
Comparing impacts of alien plants and animals in Europe using a standard scoring system (2015, methodology): “…of random effects improved model fit (i.e. that taxa differ in their impact) compared to an equivalent model without random effects fitted by generalized least squares (function gls from the package nlme, version 3 1-113; Pinheiro et al. 2013) by comparing their AICc values (Zuur et al. 2009).”
INFERENCE AFTER MODEL AVERAGING IN LINEAR REGRESSION MODELS (2018, methodology): “Both MMA-B and JMA-B perform well, and the average length of the confidence intervals of MMA-B and JMA-B is shorter than those based on JUST and ALASSO.”
of construction probability on word durations during spontaneous incremental sentence production (2012, unknown): “…software R (R Development Core Team, 2007), that allow for the simultaneous consideration of multiple covariates, while keeping under statistical control the between-speaker and between-item variance (cf. Baayen, 2008; Baayen, Davidson, & Bates, 2008; Bates & Sarkar, 2007; Pinheiro & Bates, 2000).”