Double hierarchical generalized linear models (with discussion)

By Youngjo Lee, John A. Nelder. 2006

406 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/af2325c2b76116ffa12fe2b70db426a69c605017

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In Statistics and Probability

Citations in the corpus

A Family of Generalized Linear Models for Repeated Measures With Normal and Conjugate Random Effects (pp.188) (2010, background): “Important unifying and computational progress has been made by Lee and Nelder (1996, 2001a, 2001b, 2003) (see also Lee, Nelder and Pawitan, 2006) by proposing so-called hierarchical generalized linear models, offering a broad class of outcome and random-effects distributions, combined with…”
Mixed model prediction and small area estimation (2006, methodology): “See, for example, Breslow and Clayton (1993), Lee and Nelder (1996), Christiansen and Morris (1997), Malec et al. (1997), Ghosh et al. (1998), Lahiri and Maiti (2002) and Meza (2003).”
Statistical process control for multistage processes with binary outputs (2013, methodology): “By using the results in Section 3.3 of Lee and Nelder (1996) and the preceding matrix expressions in Appendices A and B, we have the following consistent estimate for Var(̂δi ): V̂ar(δ̂) = A−111·2.”
A Mixed Model Approach for Geoadditive Hazard Regression (2007, background): “For example, Lee & Nelder (1996) suggest hierarchical generalized linear models with uncorrelated random effects based on conjugate priors.”
Generalized additive models for location, scale and shape (2005, methodology): “Lee and Nelder (1996, 2001a) assumed independent random effects, whereas Lee and Nelder (2001b) relaxed this assumption to allow correlated random effects.”
Double hierarchical generalized linear models (2006, methodology): “…(DHGLMs); these include mixed linear models (Verbeke and Molenberghs, 2000), generalized linear mixed models (Breslow and Clayton, 1993), HGLMs (Lee and Nelder, 1996), multilevel models (Goldstein, 1995), random-coefficient models (Longford, 1993), joint models of mean and dispersions Address…”
Semiparametric inference in generalized mixed effects models (2008, background): “Examples and overviews of this vast topic have been provided by Searle et al. (1982), Breslow and Clayton (1993), Lee and Nelder (1996), Malec et al.”
Maximization by Parts in Likelihood Inference (2005, methodology): “In the discussion of Lee and Nelder (1996), Clayton remarked that the analytical convenience of the hierarchical Poisson–gamma model should not obviate investigation of normal random effects in GLMMs, which are easy to interpret and have been widely used in practice.”
Estimation of generalized linear latent variable models (2004, methodology): “It should be stressed that equation (14) defines the Lee and Nelder (1996) h-likelihood.”
Latent Variable Modelling: A Survey * (2007, neutral): “Nychka, 1987; Davidian & Gallant, 1992), non-parametric maximum-likelihood estimation (e.g. Lindsay, 1995), other non-normal distributions (e.g. Lee & Nelder, 1996; Wedel & Kamakura, 2001; Lee et al., 2006) or, in a Bayesian setting, semiparametric mixtures of Dirichlet processes (e.g. Müller &…”
Nested generalized linear mixed models: an orthodox best linear unbiased predictor approach (2007, methodology): “…that obtained through other marginal modelling approaches for ‘population-averaged’ inferences on the mean; however, the BLUP approach explicitly incorporates random effects and mergers the ‘subject-specific’ and the ‘population-averaged’ inferences by using the same model (Lee and Nelder, 1996).”
Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family (2017, background): “since the full conditional distributionsare straightforward to simulate from in this setting. ForPoissoncountsthereareanumberofchoicesbesidestheLGPstrategyavailabletoincorporate dependence (e.g., see Lee and Nelder, 1974; Kotz et al., 2000; Demirhan and Hamurkaroglu, 2011, among others). For example”
Actuarial Theory for Dependent Risks: Measures, Orders and Models (2005, background): “This apparent analogous structure will be exploited further to derive powerful results in the same vein as those obtained by Lefèvre and Utev (1998) and Boutsikas and Vaggelatou (2002).”