Generalised linear mixed models (GLMMs) to investigate which
Generalised linear mixed models: likelihood and Bayesian computations with applications in epidemiology
... language which implements generic Gibbs samplers and which allows for a broad set of different applications, were initiated (Gilks et ...2009), which defined a program language for generic MCMC ...
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Identifying Trends and Patterns in Incidence of AIDS in Bangkok Using Generalised Linear Mixed Models
... generalized linear mixed models ...statistical models that can cope with a wide range of response distri- butions, including binomial, Poisson and negative binomial and, like the marginal ...
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Actuarial Statistics With Generalized Linear Mixed Models
... generalized linear models (GLMs) in actuarial statis- tics received a lot of attention, starting from the actuarial illustrations in the stan- dard text by McCullagh & Nelder ...generalized ...
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Diagnostics for generalised linear mixed models
... References to our work • Generalized multilevel structural equation modeling. Psychometrika, in press. (S.Rabe-Hesketh, A.Skrondal & A.Pickles). • Generalized latent variable modeling: Multilevel, longitudinal and ...
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Inference for generalised linear mixed models with sparse structure
... 6.2.5 Approximate likelihood ratio tests In Chapter 3, we showed that Wald tests can behave very badly in some models with sparse structure, and concluded that a likelihood ratio test should be used instead ...
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Generalised linear models
... determining vulnerability to mortality during a drought episode, for further analyses of these data see (Dwyer, Fensham et al. 2010). Survival is a binary variable, taking only two values, alive or dead. The errors may ...
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Component-based regularisation of multivariate generalised linear mixed models
... better mixed–SCGLR can reconstruct ...stable, which proves that mixed–SCGLR is able to detect an isolated predictive variable among a large number of irrelevant ...accurately mixed–SCGLR ...
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Recursive partitioning of growth curve models with generalised linear mixed-effects regression trees
... In our study, the RIS model seems preferable over the RI model, because predictive accuracy is higher and tree size is substantially lower, making the tree easier to interpret and apply in practice. Substantial ...
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Analysis of generalised mixed models for categorical data
... observations which contribute to the category counts are ...observations which contribute to contingency tables are often clustered or ...data which are gathered at successive points in time or units ...
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Effect Displays in R for Generalised Linear Models
... When a model includes interactions between or among covariates, consideration should be given to the values at which the covariates are set in effect displays and to the form of the display. Consider the following ...
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Generalised linear models for aggregate claims; to Tweedie or not?
... two models so we can multiply the frequency and severity estimators for each ...in which the first k columns correspond to the common covariates, the next k N − k columns correspond to the covariates that ...
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Effect Displays in R for Generalised Linear Models
... 3 Effect-Display Software The software in the effects package consists of several related functions, described in more specific detail in the help pages for the package given as an appendix to this paper: • The function ...
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A Diagnostic Test for the Mixing Distribution in a Generalised Linear Mixed Model
... The proposed hypothesis test of the mixing distribution based on (5) yields D = 14.29 and p = .0064. Results based on the influence function variance estimator are similar, with e D = 18.84 and p = .0008. Thus, these ...
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Fitting generalised linear models to car claims data
... ± 2 threshold values which conform to what is expected. The compound Poisson distribution The decomposition of the aggregate claim amount S paid annually by the insurer allows consideration of the number of claims ...
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Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models
... University of Southampton, Southampton, SO17 1BJ, UK ∗ E-mail: [email protected]. Abstract Multiple imputation is a commonly used approach to deal with missing values. In this approach, an imputer repeatedly imputes ...
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Generalised linear models for flexible parametric modelling of the hazard function
... There were marked differences in the extrapolations from each model, and hence estimates of lifetime mean survival. Using external evidence, only the extrapolations from one each of the DSMs and GAMs along with both FPs ...
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glm-ie: Generalised Linear Models Inference & Estimation Toolbox
... In MAP estimation, we most naturally use the penalty function ρ(s) = − ln T (s) in Equation (2) which includes penalty functions like p-norms penAbs, penQuad, penPow. Approximate inference by variational bounding ...
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AN APPLICATION OF LINEAR MIXED MODELS ON CD4+
... generalized linear models without incorporating random effects to account between subject variability may lead to incorrect ...data which measured repeatedly on same subject as it was independent; ...
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Recursive residuals for linear mixed models
... Best Linear Unbiased Predictor (BLUP) ...method, which is also known as the Ordinary Least Squares (OLS) method, is used extensively in Analysis of Variance ...from which parameters and variances are ...
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Estimation for Multivariate Linear Mixed Models
... applied linear mixed model in Statistics for Biology Systems to calculate both covariates and correlations between signals which followed non-stationary time ...
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