Negative binomial loglinear mixed models

By James G. Booth, George Casella, H. Friedl, James P. Hobert. 2003

135 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a7a4a65e275fcd8229d6582fd1b5e1fac6400835

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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, methodology): “The Poisson case received particular attention by Breslow (1984) and Lawless (1987). Molenberghs and Verbeke (2005) mention various model-based approaches that accommodate overdispersion, including the beta-binomial model (Skellam, 1948), the Bahadur model (1961), the multivariate probit model (Dale”
Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data (2018, methodology): “Therefore, the Lindley distribution can be rewritten as (Geedipally et al. 2012, Rahman Shaon and Qin 2016): ~ 2, 1 1, (4) which can be restructured as: ~∑ 1 , ; (5) The NB-L GLM can be written as the following multi-level hierarchical structure using Equations (1) to Equation (5): ; , |, ; , ∑ ~ ; ”
Exposure to air pollution and COVID-19 mortality in the United States: A nationwide cross-sectional study (2020, methodology): “We fit a negative binomial mixed model 34-36 using COVID-19 deaths as the outcome and PM 2.5 as the exposure of interest to estimate the association between COVID-19 mortality rate and long-term PM 2.5 exposure, adjusted by covariates.”
Nested generalized linear mixed models: an orthodox best linear unbiased predictor approach (2007, methodology): “…modelling methodologies, model checking has generally been ignored (see for example Zeger and Karim (1991), Breslow and Clayton (1993) and Booth et al. (2003)) or done via normality checking of residuals (Lee and Nelder, 1996); however, the justification of the normality of residuals has…”