activity
20172025
most citedConvex and non-convex regularization methods for spatial point processes intensity estimation

33 citations · 34 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME2025

Marked point processes intensity estimation using sparse group Lasso method applied to locations of lucrative and cooperative banks in mainland France

Amélie Artis, Achmad Choiruddin, Jean-François Coeurjolly +1

In this paper, we model the locations of five major banks in mainland France, two lucrative and three cooperative institutions based on socio-economic considerations. Locations of…

stat.ME2024

Examining the robustness of a model selection procedure in the binary latent block model through a language placement test data set

Vincent Brault, Frédérique Letué, Marie-José Martinez

When entering French university, the students' foreign language level is assessed through a placement test. In this work, we model the placement test results using binary latent bl…

stat.ME2021

Adaptive lasso and Dantzig selector for spatial point processes intensity estimation

Achmad Choiruddin, Jean-François Coeurjolly, Frédérique Letué

Lasso and Dantzig selector are standard procedures able to perform variable selection and estimation simultaneously. This paper is concerned with extending these procedures to spat…

stat.ME2017★ 1 cited

Spatial point processes intensity estimation with a diverging number of covariates

Achmad Choiruddin, Jean-François Coeurjolly, Frédérique Letué

Feature selection procedures for spatial point processes parametric intensity estimation have been recently developed since more and more applications involve a large number of cov…

stat.ME2017★ 33 cited

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

Achmad Choiruddin, Jean-François Coeurjolly, Frédérique Letué

This paper deals with feature selection procedures for spatial point processes intensity estimation. We consider regularized versions of estimating equations based on Campbell theo…