Abstract. Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the vari-ability in the data is not adequately described by the models, which often exhibit a prescribed mean–variance link, and (2) the accommodation of hier-archical structure in the data, stemming from clustering in the data which, in turn, may result from repeatedly measuring the outcome, for various mem-bers of the same family, etc. The first issue is dealt with through a vari...
The current work deals with modelling longitudinal or repeated non-Gaussian measurements for a respi...
When modelling multivariate binomial data, it often occurs that it is necessary to take into conside...
We combine conjugate and normal random effects in a joint model for outcomes, at least one of which ...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
AbstractNon-Gaussian outcomes are often modeled using members of the so-called exponential family. N...
Vangeneugden et al. [15] derived approximate correlation functions for longitudinal sequences of gen...
© 2013, © 2013 Taylor & Francis. Many applications in public health, medical and biomedical or oth...
© 2014 SAGE Publications. Non-Gaussian outcomes are frequently modelled using members of the exponen...
Overdispersion and correlation are two features often encountered when modeling non-Gaussian depende...
Repeated measures data refers to data sets in which observations are taken on each subject at multip...
A variety of methods of modelling overdispersed count data are compared. The methods are classified ...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
This paper presents, extends, and studies a model for repeated, overdispersed time-to-event outcomes...
The current work deals with modelling longitudinal or repeated non-Gaussian measurements for a respi...
When modelling multivariate binomial data, it often occurs that it is necessary to take into conside...
We combine conjugate and normal random effects in a joint model for outcomes, at least one of which ...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious...
AbstractNon-Gaussian outcomes are often modeled using members of the so-called exponential family. N...
Vangeneugden et al. [15] derived approximate correlation functions for longitudinal sequences of gen...
© 2013, © 2013 Taylor & Francis. Many applications in public health, medical and biomedical or oth...
© 2014 SAGE Publications. Non-Gaussian outcomes are frequently modelled using members of the exponen...
Overdispersion and correlation are two features often encountered when modeling non-Gaussian depende...
Repeated measures data refers to data sets in which observations are taken on each subject at multip...
A variety of methods of modelling overdispersed count data are compared. The methods are classified ...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
Molenberghs, Verbeke, and Demétrio (2007) and Molenberghs et al. (2010) proposed a general framework...
This paper presents, extends, and studies a model for repeated, overdispersed time-to-event outcomes...
The current work deals with modelling longitudinal or repeated non-Gaussian measurements for a respi...
When modelling multivariate binomial data, it often occurs that it is necessary to take into conside...
We combine conjugate and normal random effects in a joint model for outcomes, at least one of which ...