The generalised linear mixed model
A Diagnostic Test for the Mixing Distribution in a Generalised Linear Mixed Model
... a generalised linear mixed ...the mixed model to those from a model that conditions out the random ...the mixed-effect and conditional estimators are ...the mixed ...
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Inference for generalised linear mixed models with sparse structure
... a generalised linear mixed ...a generalised linear mixed model, there is a parameter ψ controlling how the random effects enter into the linear ...
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Diagnostics for generalised linear mixed models
... the model with parameters b θ (−j) – Obtain the statistic S j(−j) k for the simulated responses • Stata commands for simulating standardised deletion residuals under null hypothesis: postfile file res using ...
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Component-based regularisation of multivariate generalised linear mixed models
... 2 Model definition and notations In the framework of a multivariate GLMM, we consider q response–vectors y 1 , ...to model and predict Y , how many we do not ...to model Y ...
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Generalised linear mixed models: likelihood and Bayesian computations with applications in epidemiology
... generalized linear mixed model (GLMM) takes this dependency structure into account by introducing patient- specific model parameters which are called random ...
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Generalised Linear Model Trees with Global Additive Effects
... linear mixed-effects model (GLMM) fixed instead of – as in PALM tree – further fixed ...(generalised) linear models and model-based recursive partitioning, in particular LM ...
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Identifying Trends and Patterns in Incidence of AIDS in Bangkok Using Generalised Linear Mixed Models
... effects. Generalised linear mixed Poisson regression models are fitted initially and tests for overdispersion based on the ratio of Pearson residuals to the model residual degrees of freedom ...
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Recursive partitioning of growth curve models with generalised linear mixed-effects regression trees
... 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 ...
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Analysis of generalised mixed models for categorical data
... comparable model is Agresti’s ordinal-nominal model instead of ordinal-ordinal ...ordinal-nominal model, interaction effects are redefined by introducing a uniform association parameter for each row ...
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Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models
... regression model had a continuous response, we considered Box-Cox transformations where necessary to improve the normality assumptions required in the imputation modelling strategy; full details are given in ...
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Multiple imputation for missing data and statistical disclosure control for mixed-mode data using a sequence of generalised linear models
... It is important to consider how the contributions of this thesis fit the wider contexts of statistical disclosure control. Here are some thoughts on how the work can be taken forward for relevant future contributions. ...
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Fitting generalised linear models to car claims data
... are mixed together in unknown ...regression model for each segment using the expectation-maximization (EM) algorithm that maximizes the expected log-likelihood ...
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Numerically Stable Approximate Bayesian Methods for Generalized Linear Mixed Models and Linear Model Selection
... 20 1. I NTRODUCTION and one or more explanatory variables. They provide a general method to anal- yse quantified relationships between variables within a data set in an easily inter- pretable way. A standard assumption ...
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Generalised linear models
... your model, if you are looking to assess significance of explanatory variables alone and are not interested in parameter estimates then switching to F-‐tests might ...to model to get insight into process ...
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Deletion diagnostics for the linear mixed model
... At the m odel identification stage, m odels are selected that m ay be appropriate for the dataset o f interest. T he m odel will inevitably involve one or m ore param eters w hose values m ust be estim ated from the ...
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Parameter estimation and inference in the linear mixed model
... The variance–covariance matrix for the data is of the form var ( y ) = σ 2 ( ZGZ + I 108 ). In the following we present results from the fitted model. To index the observations we use the notation j . l to label ...
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Model selection in linear mixed effect models
... large model variance and a number of potentially nuisance random effect components can pose great challenge to maximum likelihood based approaches in estimating and selecting the correct ...the model using ...
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Model selection methods in the linear mixed model for longitudinal data
... the model with the next smallest G : statistic would cause the user to either omit a statistically important covariate or to add a covariate that was not statistically ...candidate model be fit in order to ...
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A Generalised Linear and Nonlinear Spline filter
... a generalised spline filter based on spline theory and M-estimation theory is ...the linear spline filter and the nonlinear robust filter have the same theoretical framework and can be deduced directly by ...
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A Generalised Model of Monopsony
... maintaining given stock of workers is increasing in employment. This may sound very similar to the usual condition for monopsony but the marginal cost of employment being increasing in the wage for a given level of ...
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