Generalized, Linear, and Mixed Models

By Charles E. McCulloch, S. R. Searle. 2005

1,856 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/9ffaf58960b059758a5bb1c5170c10c70f1c113b

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Citations in the corpus

ASREML user guide release 1.0 (2002, methodology): “McCulloch and Searle (2001) also discuss the use of PQL for GLMMs.”
Historical Accumulation of Nonindigenous Forest Pests in the Continental United States (2010, positive): “We applied Poisson regression from the family of generalized linear models (McCulloch and Searle 2001) to evaluate the number of nonindigenous insects identified over time by feeding guild (phloem and wood borers, foliage feeders, sap feeders, and other).”
Some asymptotic results on generalized penalized spline smoothing (2009, methodology): “This has the practical effect that smoothing parameter selection can now be carried out by maximum likelihood or restricted maximum likelihood estimation (see for example McCulloch and Searle (2001) or Kauermann (2005)).”
Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes (2003, background): “Traditionally, the generalized linear mixed model provides a flexible stochastic mod- eling framework that can describe complicated processes (e.g., McCulloch and Searle 2001).”
MCMC methods for multi-response generalized linear mixed models (2010, background): “However, generalizing these models to non-Gaussian data has proved difficult because integrating over the random effects is intractable (McCulloch and Searle 2001).”
Design Issues for Generalized Linear Models: A Review (2006, neutral): “sey (1997), McCulloch and Searle (2001), Dobson”
On the Unnecessary Ubiquity of Hierarchical Linear Modeling (2017, methodology): “…is an algorithmic method to estimate generalized linear models which, as briefly alluded to in the previous section, relate the conditional mean of an outcome variable distribution E Yj Xj j to a linear predictor X through a link function g (·) (McCullagh & Nelder, 1989; McCulloch & Searle, 2001).”