Generalised Linear Mixed Models
Inference for generalised linear mixed models with sparse structure
... in generalised linear mixed ...in models in which there is no obvious simplification to the high-dimensional integral involved in the ...sparse models, in which little information is ...
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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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Component-based regularisation of multivariate generalised linear mixed models
... better mixed–SCGLR can reconstruct ...that mixed–SCGLR is able to detect an isolated predictive variable among a large number of irrelevant ...accurately mixed–SCGLR recovers ϕ 1 and ϕ 2 , it ...
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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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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 models for reading scores we found gender not to be a partitioning variable of high importance, but instead socioeconomic status was an important predictor for growth ...
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Analysis of generalised mixed models for categorical data
... threshold models are ...threshold models only utilise the ordered relationship in the categories of the response variable the comparable model is Agresti’s ordinal-nominal model instead of ordinal-ordinal ...
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A Diagnostic Test for the Mixing Distribution in a Generalised Linear Mixed Model
... conditional maximum likelihood estimate, and we used relatively straightforward algebraic arguments presented in the appendix to derive the asymptotic variance of the test statistic based on this difference. This ...
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Fitting generalised linear models to car claims data
... class models. These models assume that the observed data are actually composed of several homogeneous segments that are mixed together in unknown ...these models over traditional clustering ...
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Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models
... regression models as described in Section ...imputation models has been recommended by Little (1992), although we note that others have suggested the response should not be included (D’Agostino & Rubin ...
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Multiple imputation for missing data and statistical disclosure control for mixed-mode data using a sequence of generalised linear models
... univariate linear regression models are assumed to be normally and independently distributed, each with zero mean and com- mon ...imputation models (see Appendix B for ...the linear regression ...
195
Some problems in estimation in mixed linear models
... Fellner's method is also a direct robustification of the REML estimating equations. We then apply these estimators to two data sets. The robust estimators proposed in the third chapter are modified in the fourth chapter ...
163
Generalised linear models
... (Crawley 2007; Gelman and Hill 2007; Bolker 2008)) and I refer readers to these for complementary material. While students who have had experience with GLMs generally have a good grasp of the “how” to undertake a GLM ...
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Effect Displays in R for Generalised Linear Models
... By default, the print and summary methods express effects on the scale of the response, but this behaviour may be modified via the argument type. In general, I find tabular displays of effects less satisfactory than ...
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Generalised linear models for aggregate claims; to Tweedie or not?
... Similarly, let us assume that k Y covariates are retained for the severity model and let β Y be its vector of coefficients. Since we might have used different covariates for each model, we need to unify the portfolio ...
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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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Generalized Linear Mixed Models
... This example shows that the significance of model terms can depend on the structure of the random effects. Thus, one must decide upon a reasonable model for the random effects as well as for the fixed effects. A commonly ...
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Linear mixed models in Stata
... Panel representation Classical representation has roots in the design literature, but can make model specification hard When the data can be thought of as M independent panels, it is more convenient to express the ...
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Gradient test for generalised linear models with random effects.
... 2 Universidade Federal do Rio Grande do Norte, Natal, Brazil E-mail for correspondence: [email protected] Abstract: This work develops the gradient test for parameter selection in gen- eralised ...
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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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