Generalized, Linear, and Mixed Models

By M. V. Tress. Technometrics, 2003

1,787 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c834f3b320049bb0405071a2792caae7263fc2c9

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Multilevel modelling of complex survey data (2006, background): “Pfeffermann et al. (1998), Skinner and Holmes (2003) and Grilli and Pratesi (2004)), our approach can handle multilevel models with any number of levels, as well as allowing for stratification and PSUs that are not represented by a random effect in the model.”
Metropolis-Hastings Robbins-Monro Algorithm for Confirmatory Item Factor Analysis (2010, methodology): “This is again a familiar sight because the log likelihood 13 is widely used in structural equation modeling (see, e.g., Browne & Arminger, 1995) or normal theory mixed models (see, e.g., McCulloch & Searle, 2001).”
Modeling Clustered Data with Very Few Clusters (2016, background): “|X j) = μ j to a linear predictor X jβ through a link function g (·) (McCullagh & Nelder, 1989; McCulloch & Searle, 2001).”
Actuarial statistics with generalized linear mixed models (2007, background): “Diggle et al. (2002), McCulloch & Searle (2001), Demidenko (2004) and Molenberghs & Verbeke (2005) are useful references for full details on GLMMs.”
General design Bayesian generalized linear mixed models (2006, background): “As summarized in Section 9.6 of McCulloch and Searle (2001), the frequentist approach to this problem is thwarted by largely intractable distribution theory.”
Exploring Local Item Dependence Using a Random-Effects Facet Model (2005, background): “The MRCMLM (see Equation (23)), being a member of the exponential family of distributions, can be viewed as a generalized linear mixed model (De Boeck & Wilson, 2004; McCullagh & Nelder, 1989; McCulloch & Searle, 2001; Nelder & Wedderburn, 1972; Rijmen, Tuerlinckx, De Boeck, & Kuppens, 2003).”
Factors affecting detection probability in plant distribution studies (2009, methodology): “For inference about the fixed effects, we used the Wald statistic (McCulloch & Searle 2001).”
Applied Multivariate Analysis (2002, methodology): “Searle et al. (1992), McCulloch and Searle (2001), and Khuri et al. (1998) provide an extensive review of these models.”
Random effect models for repeated measures of zero-inflated count data (2005, methodology): “There are many methods to approximate the ML estimate for generalized linear mixed models (GLMM) (Fahrmeir and Tutz, 2001; McCulloch and Searle, 2001) such as Gauss–Hermite quadrature, the Monte Carlo EM algorithm, Markov chain Monte Carlo, PQL and Laplace approximations.”
The multilevel latent covariate model: a new, more reliable approach to group-level effects in contextual studies. (2008, methodology): “(3) This notation is referred to as the linear mixed-effect notation (McCulloch & Searle, 2001) and is used, for example, by the mixed module in SPSS and similar procedures in other statistical packages.”
Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models (2008, methodology): “In general, for mixed linear models asymptotic limits are obtained either when the number of observations in each small area tends to inﬁnity, or when the number of small areas tend to inﬁnity; see McCulloch and Searle (2001) and Rao (2003) for details.”