Modelling and analysing correlated non-normal data

By Youngjo Lee, John A. Nelder. 2001

61 citations recorded by the source corpus.

Subfields: Environmental Engineering, Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f786055c201cdd958c091c98b52aab98ca09bad4

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In Environmental Engineering

In Genetics

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Citations in the corpus

Small Area Estimation Under Linear and GeneralizedLinear Mixed Models With Time and Area Effects (2003, background): “Lee and Nelder (2001a, 2001b) further extend the work of Nelder and Lee (1996) to correlated non-normal data.”
Double hierarchical generalized linear models (2006, methodology): “This method works generally well in various models: see the simulation studies of Poisson and binomial models with moderately large binomial denominator (Lee and Nelder, 2001a), of frailty models (Ha et al., 2001) and of mixed linear models with censoring (Ha et al., 2002).”
Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family (2017, background): “However, the current HGLM literature specifies an LGP for the dependent data setting (Lee and Nelder, 2000, 2001).”
Double hierarchical generalized linear models (with discussion) (2006, methodology): “This method works generally well in various models: see the simulation studies of Poisson and binomial models with moderately large binomial denominator (Lee and Nelder, 2001a), of frailty models (Ha et al., 2001) and of mixed linear models with censoring (Ha et al., 2002).”
Estimating Frailty Models via Poisson Hierarchical Generalized Linear Models (2003, methodology): “For models with lognormal frailty the rst-order Laplace approximation is often enough, while for models with nonlognormal frailty such as gamma frailty we need the second-order Laplace approximation (Lee and Nelder, 2001a) to reduce the bias.”
Testing environmental and genetic effects in the presence of spatial autocorrelation (2014, methodology): “Dispersion parameters were estimated using leverages corrected as in Lee and Nelder (2001a).”
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.”
Conditional and Marginal Models: Another View (2004, methodology): “In these models joint optimization of theh-likelihood offers a numerically and statistically efficient fitting algorithm (Lee and Nelder, 2001a).”