Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns
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Scholar |
Altres documents de l'autoria: González, Jonatan A.; Rodríguez-Cortés, Francisco Javier; Romano, Elvira; Mateu, Jorge
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Mostra el registre complet de l'elementcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
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https://doi.org/10.1007/s13253-021-00455-1 |
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Títol
Classification of Events Using Local Pair Correlation Functions for Spatial Point PatternsData de publicació
2021-05-12Editor
American Statistical Association; International Biometrics Society; Springer VerlagISSN
1085-7117; 1537-2693Cita bibliogràfica
González, J.A., Rodríguez-Cortés, F.J., Romano, E. et al. Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns. JABES (2021). https://doi.org/10.1007/s13253-021-00455-1Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://www.springer.com/statistics/life+sciences%2C+medicine+%26+health/journal/13253Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the second-order properties of the point ... [+]
Spatial point pattern analysis usually concerns identifying features in an observation window where there is also noise. This identification traditionally begins with studying the second-order properties of the point pattern, and it may be done locally by using local second-order characteristics (LISA). Some properties of this local structure solve the problem of classification into feature and clutter points. This paper proposes an estimator for local pair correlation LISA functions, discusses some of its properties and considers a particular distance to measure dissimilarities. Two classification procedures to separate feature from clutter points are described. One of them adopts multidimensional scaling and support vector machines, and the other employs bagged clustering. Simulations demonstrate the performance of the method, and it is applied to a dataset concerning earthquakes in a seismic nest located in Colombia. [-]
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