Convex and non-convex regularization methods for spatial point processes intensity estimation - [Labex] PERSYVAL-lab
Journal Articles Electronic Journal of Statistics Year : 2018

Convex and non-convex regularization methods for spatial point processes intensity estimation

Abstract

This paper deals with feature selection procedures for spatial point processes intensity estimation. We consider regularized versions of estimating equations based on Campbell theorem derived from two classical functions: Poisson likelihood and logistic regression likelihood. We provide general conditions on the spatial point processes and on penalty functions which ensure consistency, sparsity and asymptotic normality. We discuss the numerical implementation and assess finite sample properties in a simulation study. Finally, an application to tropical forestry datasets illustrates the use of the proposed methods.
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Dates and versions

hal-01484779 , version 1 (09-03-2017)
hal-01484779 , version 2 (24-08-2018)

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Cite

Achmad Choiruddin, Jean-François Coeurjolly, Frédérique Letué. Convex and non-convex regularization methods for spatial point processes intensity estimation. Electronic Journal of Statistics , 2018, 12 (1), pp.1210-1255. ⟨10.1214/18-EJS1408⟩. ⟨hal-01484779v2⟩
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