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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A joint model for longitudinal continuous and time-to-event outcomes with direct marginal interpretation.
TL;DR: This paper proposes a so-called marginalized joint model for longitudinal continuous and repeated time-to-event outcomes on the one hand and a marginalized joint models for bivariate repeated time to event outcomes onThe other, which can be fitted relatively easily using standard statistical software.
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A Marginalized Model for Zero-Inflated, Overdispersed, and Correlated Count Data
Geert Molenberghs,Samuel Iddi +1 more
TL;DR: A marginalized, zero-inflated, overdispersed model for correlated count data is proposed and it is shown that the proposed model leads to important improvements in model fit.
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A hierarchical Bayesian approach for the analysis of longitudinal count data with overdispersion
TL;DR: To compare the performance of the two models, a simulation study is conducted in which the mean squared error, relative bias, and variance of the posterior means are compared.
Journal ArticleDOI
Marginalized zero-inflated generalized Poisson regression.
Felix Famoye,John S. Preisser +1 more
TL;DR: A marginalized ZIGP is proposed to directly model the population marginal mean count by estimating the parameters of the marginalized zero-inflated GP model by the method of maximum likelihood.
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Statistical model for overdispersed count outcome with many zeros: an approach for direct marginal inference
TL;DR: In this paper, the authors developed a marginalized model for zero-inflated univariate count outcome in the presence of overdispersion, which is implemented using the SAS NLMIXED procedure with minimal coding efforts.
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).
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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.