When studying a forest disease, understanding the phenological relationship of host tree and its pathogens is essential for identifying optimal management strategies to help prevent future spread disease. Since beech leaf disease (BLD) recently discovered information about general epidemiology symptom phenology largely unavailable. This study sought answer questions related progression by condu...

In an interesting article Wieteke van Dijk and colleagues argue that societal developments and values influence the practice of medicine, and thus can result in both medicalisation and overdiagnosis. They provide a convincing argument that overdiagnosis emerges in a social context and that it has socially constructed implications. However, they fail to show that overdiagnosis per se is socially...

The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow beha...

We use spatial generalized linear mixed models (GLMM) to model non-Gaussian spatial variables that are observed at sampling locations in a continuous area. In many applications, prediction of random effects in a spatial GLMM is of great practical interest. We show that the minimum mean-squared error (MMSE) prediction can be done in a linear fashion in spatial GLMMs analogous to linear kriging. ...

Abstract Generalized linear mixed models form a general class of random effects models for discrete and continuous response in the exponential family. Spatial GLMM are an extension of such models that allows us to fit spatial-dependent data. A popular model in this class is the probit-normal model. In this study we develop a novel exact algorithm to estimate a probit spatial generalized linear ...

OBJECTIVE For many applied problems in the context of medically relevant artificial intelligence, the data collected exhibit a hierarchical or clustered structure. Ignoring the interdependence between hierarchical data can result in misleading classification. In this paper, we extend the mechanism for mixture-of-experts (ME) networks for binary classification of hierarchical data. Another exten...

The generalized linear mixed models (GLMMs) for clustered data are studied when covariates aremeasured with error. Themost conventional measurement error models are based on either linear mixed models (LMMs) or GLMMs. Even without the measurement error, the frequentist analysis of LMM, and particularly of GLMM, is computationally difficult. On the other hand, Bayesian analysis of LMM and GLMM i...

Introduction These are (incomplete) course notes about generalised linear mixed models (GLMM). Special emphasis is placed on understanding the underlying structure of a GLMM in order to show that slight modifications of this structure can produce a wide range of models. These include familiar models like regression and ANOVA, but also models with intimidating names: animal models, threshold mod...