Generalised Linear Models and its Extensions
Generalised linear models for flexible parametric modelling of the hazard function
... parameter models are available, including the exponential, Weibull, Gompertz, log-logistic and lognormal ...these models may not be sufficiently flexible to capture complex, time-varying hazards [6, ...
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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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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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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?
... explain its role with an example; suppose that we are interested in modelling the annual aggregate loss for a portfolio of insurance policies that is already divided into homogeneous ...
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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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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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Fitting generalised linear models to car claims data
... Abstract: Generalised linear models (GLMs) overcome the limitations of Normal regression models since they can accommodate any distribution that is a member of the exponential ...These ...
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Inference for generalised linear mixed models with sparse structure
... some models with sparse structure, and concluded that a likelihood ratio test should be used instead wherever ...of its use in the sequential reduction method for approximating the ...
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Component-based regularisation of multivariate generalised linear mixed models
... 2 kα 3 (ϕ 1 + ϕ 2 )k 2 , where b β (b) j is the fixed–effect estimate related to response y j associated with sample b. Table 6 summarises the values of the afore–defined criteria and presents biases and standard errors ...
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glm-ie: Generalised Linear Models Inference & Estimation Toolbox
... in generalised linear mod- els over continuous-valued ...graphical models or Markov random fields with Gaussian and non-Gaussian potentials is achieved by casting all the computations as matrix ...
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New Methodologies for Differential-Linear Cryptanalysis and Its Extensions
... 7-round linear approximation, however, there are a large number of possible differential characteristics for which calculating the probability of β γ = 0 is beyond the computational power of a general ...
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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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Markov chain Monte Carlo methodoloy for inference with generalised linear spatial models
... Under the Bayesian framework, inference on the latent process and the parameters of the model relies on the use of MCMC methods since direct sampling from their joint posterior distribut[r] ...
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Extensions to Bayesian generalized linear mixed effects models for household tuberculosis transmission
... standard models compared to the proposed method, and risk of TB infection from outside the home increases with age stratum, reflecting longer at-risk duration for community-based exposure for older ...
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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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Poverty Diagnostics in the Philippines: Assessing Impacts of Programs through Generalised Linear Models (GLMs)
... Chapter 3 Analysis Techniques and methods have been discussed in Chapter 2 to analyse sev- eral aspects relating to dynamics in poverty among families. The results in applying these techniques will be discussed in this ...
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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 are ...
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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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Modelling the effects of air pollution on health using Bayesian dynamic generalised linear models
... trend models β t Long-term trends, overdispersion and temporal correlation are removed from the health data with one of four trend models: a natural cubic spline of calendar time (models 1 and 2); a ...
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