Generalized Linear Mixed Models

By Charles E. McCulloch, J. Neuhaus. 2005

1,141 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Statistics and Probability

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Citations in the corpus

Modeling Consumer Preferences and Price Sensitivities from Large-Scale Grocery Shopping Transaction Logs (2017, background): “The next term can be regarded as an analogy of the random coefficient model [22,25,36,39], which involves explicit features whose”
Variable Selection in Heterogeneous Datasets: A Truncated-rank Sparse Linear Mixed Model with Applications to Genome-wide Association Studies (2017, methodology): “Before we introduce our method, we first revisit the classical linear mixed model [26].”
On the Origin of Deep Learning (2017, methodology): “elationship is critical (Lippert et al., 2011; Zhou and Stephens, 2012). With this critical usage, many more generalized version of Linear Mixed Models have been introduced in the linear cases, like (McCulloch and Neuhaus, 2001; Nguyen et al., 2008; Wang and Yang, 2016) 7.3.2 Select-Additive Network”
A General Method for Robust Bayesian Modeling (2015, background): “Note these models can be seen as instances of random intercept generalized linear models (Rabe-Hesketh and Skrondal, 2008; McCulloch and Neuhaus, 2001).”
Applied Linear Regression (2004, background): “Useful and more comprehensive references include Fitzmaurice et al. (2011), Goldstein (2010), McCulloch et al. (2008), Raudenbush and Bryk (2002), and Zuur et al. (2009).”
SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training (2021, neutral): “When one has reasonable expectations of linear relationships, a variety of modeling approaches are available [28, 37, 44, 45].”
GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction (2016, methodology): “Generalized linear mixed models (GLMix) have been thoroughly studied in Statistics [15] and widely applied in many applications.”
The role of administrative data in the big data revolution in social science research. (2016, methodology): “Multivariate methods, for example from the generalized linear mixed model family, provide improved statistical control for analyses of observational social science data (see Hedeker, 2005).”
Bayesian inference for generalized linear mixed models of portfolio credit risk (2007, methodology): “For a detailed Bayesian interpretation of GLMMs in this context, see Clayton (1996). The posterior distribution of θ will in particular contain posterior information about bt; a fact which we exploit in Section 3.”
Maximin effects in inhomogeneous large-scale data (2014, background): “Mixed- and randomeffects models [McCulloch (2006), Pinheiro and Bates (2000)] are related but do not have an observation-specific random effect.”