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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)

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)

- Robin Loche, Benoit Giron, David Abrial, Lionel Cucala, Myriam Charras
- 2015

Description Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.

- Lionel Cucala
- 2005

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)

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