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2018 Convex and non-convex regularization methods for spatial point processes intensity estimation
Achmad Choiruddin, Jean-François Coeurjolly, Frédérique Letué
Electron. J. Statist. 12(1): 1210-1255 (2018). DOI: 10.1214/18-EJS1408

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. In particular, we consider two classical functions: the Poisson likelihood and the logistic regression likelihood. We provide general conditions on the spatial point processes and on penalty functions which ensure oracle property, consistency, and asymptotic normality under the increasing domain setting. We discuss the numerical implementation and assess finite sample properties in simulation studies. Finally, an application to tropical forestry datasets illustrates the use of the proposed method.

Citation

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Achmad Choiruddin. Jean-François Coeurjolly. Frédérique Letué. "Convex and non-convex regularization methods for spatial point processes intensity estimation." Electron. J. Statist. 12 (1) 1210 - 1255, 2018. https://doi.org/10.1214/18-EJS1408

Information

Received: 1 March 2017; Published: 2018
First available in Project Euclid: 29 March 2018

zbMATH: 06864490
MathSciNet: MR3780731
Digital Object Identifier: 10.1214/18-EJS1408

Subjects:
Primary: 60G55, 62H11, 62J07, 65C60, 97K80

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Vol.12 • No. 1 • 2018
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