A combined overdispersed and marginalized multilevel model
Samuel Iddi,Geert Molenberghs +1 more
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TLDR
It turns out that by explicitly allowing for overdispersion random effect, the model significantly improves and is applied to two clinical studies and compared to the existing approach.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2012-06-01 and is currently open access. It has received 26 citations till now. The article focuses on the topics: Quasi-likelihood & Overdispersion.read more
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Hierarchical models with normal and conjugate random effects: a review (invited article)
TL;DR: A unified treatment of the model framework and key extensions is provided and the basic models and several extensions are illustrated using a set of key examples, one per data type (count, binary, multinomial, ordinal, and time-to-event).
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A taxonomy of mixing and outcome distributions based on conjugacy and bridging
TL;DR: This paper contrasts the bridging and conjugate approaches to the generalized linear mixed model, and shows that only the Gaussian and degenerate distributions have well-defined cumulant generating functions for which self-bridging holds.
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A Marginalized Combined Gamma Frailty and Normal Random-effects Model for Repeated, Overdispersed, Time-to-event Outcomes
TL;DR: In this paper, the authors proposed a marginalized model for repeated or otherwise hierarchical, overdispersed time-to-event outcomes, adapting the so-called combined model of Molenberghs et al. (in press), who combined gamma and normal random effects.
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First-order marginalised transition random effects models with probit link function
Ozgur Asar,Ozlem Ilk +1 more
TL;DR: A three-level marginalised model for analysis of multivariate longitudinal binary outcome is proposed and the implicit function theorem is introduced to approximately solve the marginal constraint equations explicitly.
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Marginally specified models for analyzing multivariate longitudinal binary data
Ozgur Asar,Ozlem Ilk +1 more
TL;DR: The implicit function theorem is introduced to approximately solve the marginal constraint equations explicitly and the use of \textit{probit} link enables direct solutions to the convolution equations.
References
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Book
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).
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Longitudinal data analysis using generalized linear models
Kung Yee Liang,Scott L. Zeger +1 more
TL;DR: In this article, an extension of generalized linear models to the analysis of longitudinal data is proposed, which gives consistent estimates of the regression parameters and of their variance under mild assumptions about the time dependence.
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Categorical Data Analysis
TL;DR: In this article, categorical data analysis was used for categorical classification of categorical categorical datasets.Categorical Data Analysis, categorical Data analysis, CDA, CPDA, CDSA
Journal ArticleDOI
Generalized Linear Models
TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.