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The k-nearest-neighbour procedure is a well-known deterministic method used in supervised classification. This paper proposes a reassessment of this approach as a statistical technique derived from a proper probabilistic model; in particular, we modify the assessment made in a previous analysis of this method undertaken by Holmes underlying probabilistic… (More)
A new method is proposed for identifying clusters in spatial point processes. It relies on a specific ordering of events and the definition of area spacings which have the same distribution as one-dimensional spacings. Then the spatial clusters are detected using a scan statistic adapted to the analysis of one-dimensional point processes. This flexible… (More)
In this article we propose a new technique for identifying clusters in temporal point processes. This relies on the comparision between all the m -order spacings and it is totally independent of any alternative hypothesis. A recursive procedure is introduced and allows to identify multiple clusters independently. This new scan statistic seems to be more… (More)
Selecting between different dependency structures of hidden Markov random field can be very challenging , due to the intractable normalizing constant in the likelihood. We answer this question with approximate Bayesian computation (ABC) which provides a model choice method in the Bayesian paradigm. This comes after the work of Grelaud et al. (2009) who… (More)
Description Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.
The definition of spacings associated to a sequence of random variables is extended to the case of random vectors in [0, 1] 2. Beirlant & al. (1991) give an alternative proof of the Le Cam (1958) theorem concerning asymptotic normality of additive functions of uniform spacings in [0, 1]. I adapt their technique to the two-dimensional case, leading the way… (More)