Convex and non-convex regularization methods for spatial point processes intensity estimation
Year: 2018
Type: article
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.
Source: Electronic Journal of Statistics
DOI: 10.1214/18-ejs1408
Authors: Achmad Choiruddin, Jean-François Coeurjolly, Frédérique Letué
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $675
Awards: G2663032626, G2774424170, An International Training Workshop on Tropical Soil Properties and Hydrology Linked to Existing Forest Dynamics Projects; Barro Colorado Island, Panama; September 2003, G4426629300, G4894607620, Collaborative Research: Effects of Soil-Borne Resources on the Structure and Dynamics of Lowland Tropical Forests, Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique, G6886224072, Collaborative Research: Effects of Soil-Borne Resources on the Structure and Dynamics of Lowland Tropical Forests
OpenAlex ID: W2610761555