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Gaussian processes (GP) have proven to be useful and versatile stochastic models in a wide variety of applications including computer experiments, environmental monitoring, hydrology and climate modeling. A GP model is determined by its mean and covariance functions. In most cases, the mean is...
For count responses, the situation of excess zeros (relative to what standard models allow) often occurs in biomedical and sociological applications. Modeling repeated measures of zero-inflated count data presents special challenges. This is because in addition to the problem of extra zeros, the...
The purpose of this paper is to use the framework of hidden Markov chains (HMCs) for the modelling of the failure and debugging process of software, and the prediction of software reliability. The model parameters are estimated using the forward-backward expectation maximization algorithm, and...
In the presence of continuous covariates, standard capture-recapture methods assume either that the registrations operate independently at the individual level or that the covariates can be stratified and log-linear models fitted, permitting the modelling of dependence between data sources. This...
Finite mixtures of generalized linear mixed effect models are presented to handle situations where within-cluster correlation and heterogeneity (subpopulations) exist simultaneously. For this class of model, we consider maximum likelihood (ML) as our main approach to estimation. Owing to the...
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