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In this paper we extend Geweke's approach of Granger causality by deriving a nonlinear framework based on functional regression in reproducing kernel Hilbert spaces (RKHS). After giving the definitions of dynamical and instantaneous causality in the Granger sense, we review Geweke's measures. These measures quantify improvement in predicting a time series when the past of another one is taken into...
Granger causality considers the question of whether two time series exert causal influences on each other. Causality testing usually relies on prediction, i.e., if the prediction error of the first time series is reduced by taking measurements from the second one into account, then the latter is said to have a causal influence on the former. In this paper, a nonparametric framework based on functional...
Marked point processes have received a great attention in the recent years, for their ability to extract objects in large data sets as those obtained in biological studies or hyperspectral remote sensing frameworks. This paper focuses on an original Bayesian point process estimation for the detection of galaxies from the hyperspectral data ‘cubes’ provided by the Multi Unit Spectroscopic Explorer...
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