Similarity measures of conditional intensity functions to test separability in multidimensional point processes
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http://dx.doi.org/10.1007/s00477-012-0654-1 |
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Títol
Similarity measures of conditional intensity functions to test separability in multidimensional point processesData de publicació
2013-07Editor
Springer-VerlagISSN
1436-3240Cita bibliogràfica
Stochastic Environmental Research and Risk Assessment, 27, 5, p. 1193-1205Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
http://link.springer.com/article/10.1007/s00477-012-0654-1Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
Separability in the context of multidimensional point processes assumes a multiplicative form for the conditional intensity function. This hypothesis is especially convenient since each component of a separable process ... [+]
Separability in the context of multidimensional point processes assumes a multiplicative form for the conditional intensity function. This hypothesis is especially convenient since each component of a separable process may be modeled and estimated individually, and this greatly facilitates model building, fitting, and assessment. This is also related to the problem of reduction in the number of dimensions. Following previous approximations to this problem, we focus on the conditional intensity function, by considering nonparametric kernel-based estimators. Our approach calculates thinning probabilities under the conditions of separability and nonseparability and compares them through divergence measures. Based on Monte Carlo experiments, we approximate the statistical properties of our tests under a variety of practical scenarios. An application on modeling the spatio-temporal first-order intensity of forest fires is also developed. [-]
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