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Summary. A multivariate non‐linear time series model for road safety data is presented. The model is applied in a case‐study into the development of a yearly time series of numbers of fatal accidents (inside and outside urban areas) and numbers of kilometres driven by motor vehicles in the Netherlands between 1961 and 2000. The model accounts for missing entries in the disaggregated numbers of kilometres...
Summary. In integrated population modelling in ecology, where data from multiple surveys are analysed simultaneously, the Kalman filter may be used to approximate a component likelihood for a state space model of population count data. We evaluate a new method for initiating this Kalman filter, based on a stable age distribution. The new method is illustrated and compared with alternative approaches...
We consider Bayesian inference for stochastic differential equation mixed effects models (SDEMEMs) exemplifying tumour response to treatment and regrowth in mice. We produce an extensive study on how an SDEMEM can be fitted by using both exact inference based on pseudo‐marginal Markov chain Monte Carlo sampling and approximate inference via Bayesian synthetic likelihood (BSL). We investigate a two‐compartments...
Spatio‐temporal counts of infectious disease cases often contain an excess of zeros. With existing zero‐inflated count models applied to such data it is difficult to quantify space‐time heterogeneity in the effects of disease spread between areas. Also, existing methods do not allow for separate dynamics to affect the reemergence and persistence of the disease. As an alternative, we develop a new...
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