Journal ArticleDOI
Overdispersion: models and estimation
TLDR
In this article, different formulations for the overdispersion mechanism can lead to different variance functions which can be placed within a general family of estimation methods, including maximum likelihood, moment methods, extended quasi-likelihood, pseudo-like likelihood and non-parametric maximum likelihood.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 1998-04-03. It has received 463 citations till now. The article focuses on the topics: Quasi-likelihood & Overdispersion.read more
Citations
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Negative Binomial Regression
TL;DR: In this article, the authors introduce the concept of risk in count response models and assess the performance of count models, including Poisson regression, negative binomial regression, and truncated count models.
Book
Generalized Linear Models and Extensions
James W. Hardin,Joseph Hilbe +1 more
TL;DR: In this paper, the authors present a detailed overview of generalized linear models for estimating variance matrices in a generalised linear model (GLM) using an offset in a GLM.
Journal ArticleDOI
Zero tolerance ecology: improving ecological inference by modelling the source of zero observations
Tara G. Martin,Brendan A. Wintle,Jonathan R. Rhodes,Petra M. Kuhnert,Scott A. Field,Samantha Low-Choy,Andrew J. Tyre,Hugh P. Possingham +7 more
TL;DR: A framework for understanding how zero-inflated data sets originate and deciding how best to model them is proposed and the different kinds of zeros that occur in ecological data are defined and classified.
Book
Mixed Models: Theory and Applications
TL;DR: This paper presents a meta--analysis model for the growth curve of the Tumor Regrowth Curves of the LME Model and some of the properties of the model suggest that this model is likely to be biased towards growth curve-based models.
References
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Generalized Linear Models
Peter McCullagh,John A. Nelder +1 more
TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
BookDOI
Markov Chain Monte Carlo in Practice
TL;DR: The Markov Chain Monte Carlo Implementation Results Summary and Discussion MEDICAL MONITORING Introduction Modelling Medical Monitoring Computing Posterior Distributions Forecasting Model Criticism Illustrative Application Discussion MCMC for NONLINEAR HIERARCHICAL MODELS.
Journal ArticleDOI
Generalized linear models. 2nd ed.
Peter McCullagh,John A. Nelder +1 more
TL;DR: A class of statistical models that generalizes classical linear models-extending them to include many other models useful in statistical analysis, of particular interest for statisticians in medicine, biology, agriculture, social science, and engineering.
Journal ArticleDOI
Generalized Linear Models
TL;DR: Generalized linear models, 2nd edn By P McCullagh and J A Nelder as mentioned in this paper, 2nd edition, New York: Manning and Hall, 1989 xx + 512 pp £30