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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Marginalized models for right-truncated and interval-censored time-to-event data
TL;DR: These two approaches are investigated, and the conditional Generalized Linear Mixed Model (GLMM), in the context of right-truncated, interval-censored time-to-event data, further characterized by clustering and additional overdispersion, are applied to modeling the hazard function for the survival endpoints.
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Effect of covariate misspecifications in the marginalized zero-inflated Poisson model
Samuel Iddi,Esther O. Nwoko +1 more
TL;DR: The effects of misspecification of components of the MZIP regression model are investigated through a comprehensive simulation study and it was observed that omissions in both parts of the models lead to biases in the estimated parameters.
Marginalized zero-inflated Poisson regression
TL;DR: Long et al. as mentioned in this paper developed a marginalized zero-inflated Poisson regression model for independent responses to model the population mean count directly, allowing straightforward inference for overall exposure effects and easy accommodation of offsets representing individuals' risk times, as well as empirical robust variance estimation for overall log incidence density ratios.
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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).
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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
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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.