6533b851fe1ef96bd12aa1e1
RESEARCH PRODUCT
Spatio‐temporal classification in point patterns under the presence of clutter
Marianna SiinoGiada AdelfioFrancisco J. Rodríguez-cortésJorge Mateusubject
Statistics and Probability010504 meteorology & atmospheric sciencesComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONContext (language use)01 natural sciences010104 statistics & probabilitySpatio-temporalpoint patternsClutterExpectation–maximization algorithmEuclidean geometryEarthquakesPoint (geometry)clutter earthquakes EM algorithm features mixtures nearest‐neighbor distances spatio‐temporal point patterns0101 mathematicsEM algorithmFeatures0105 earth and related environmental sciencesspatio-temporal point patternSpatial contextual awarenessEcological Modelingmixturenearest-neighbor distanceComputingMethodologies_PATTERNRECOGNITIONearthquakeMixturesProbability distributionClutterfeatureSettore SECS-S/01 - StatisticaclutterNearest-neighbor distancesAlgorithmdescription
We consider the problem of detection of features in the presence of clutter for spatio-temporal point patterns. In previous studies, related to the spatial context, Kth nearest-neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation-maximization algorithm. This paper extends this methodology to the spatio-temporal context by considering the properties of the spatio-temporal Kth nearest-neighbor distances. For this purpose, we make use of a couple of spatio-temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions of such Kth nearest-neighbor distances and present an intensive simulation study together with an application to earthquakes.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-08-23 | Environmetrics |