Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******

Point processes are stochastic models generating interacting points or events in time and/or space. Among characteristics of these models, first-order intensity and conditional intensity functions are often considered. We focus on inhomogeneous parametric forms of these functions assumed to depend o...

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Main Authors: Coeurjolly Jean-Francois, Ba Ismaïla, Choiruddin Achmad
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:ESAIM: Proceedings and Surveys
Online Access:https://www.esaim-proc.org/articles/proc/pdf/2025/03/proc20258001.pdf
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author Coeurjolly Jean-Francois
Ba Ismaïla
Choiruddin Achmad
author_facet Coeurjolly Jean-Francois
Ba Ismaïla
Choiruddin Achmad
author_sort Coeurjolly Jean-Francois
collection DOAJ
description Point processes are stochastic models generating interacting points or events in time and/or space. Among characteristics of these models, first-order intensity and conditional intensity functions are often considered. We focus on inhomogeneous parametric forms of these functions assumed to depend on a certain number of spatial covariates. When this number of covariates is large, we are faced with a high-dimensional problem. This paper provides an overview of these questions and existing solutions based on regularizations.
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issn 2267-3059
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publishDate 2025-01-01
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series ESAIM: Proceedings and Surveys
spelling doaj-art-035e4ccb02d34990b4c40f2a3e1b054f2025-08-20T01:51:54ZengEDP SciencesESAIM: Proceedings and Surveys2267-30592025-01-018021610.1051/proc/202580002proc20258001Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******Coeurjolly Jean-Francois0Ba Ismaïla1Choiruddin Achmad2Univ. Grenoble Alpes, LJKDepartment of Mathematics and Statistics, York UniversityDepartment of Statistics, Institut Teknologi Sepuluh NopemberPoint processes are stochastic models generating interacting points or events in time and/or space. Among characteristics of these models, first-order intensity and conditional intensity functions are often considered. We focus on inhomogeneous parametric forms of these functions assumed to depend on a certain number of spatial covariates. When this number of covariates is large, we are faced with a high-dimensional problem. This paper provides an overview of these questions and existing solutions based on regularizations.https://www.esaim-proc.org/articles/proc/pdf/2025/03/proc20258001.pdf
spellingShingle Coeurjolly Jean-Francois
Ba Ismaïla
Choiruddin Achmad
Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******
ESAIM: Proceedings and Surveys
title Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******
title_full Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******
title_fullStr Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******
title_full_unstemmed Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******
title_short Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation******
title_sort regularization techniques for inhomogeneous spatial point processes intensity and conditional intensity estimation
url https://www.esaim-proc.org/articles/proc/pdf/2025/03/proc20258001.pdf
work_keys_str_mv AT coeurjollyjeanfrancois regularizationtechniquesforinhomogeneousspatialpointprocessesintensityandconditionalintensityestimation
AT baismaila regularizationtechniquesforinhomogeneousspatialpointprocessesintensityandconditionalintensityestimation
AT choiruddinachmad regularizationtechniquesforinhomogeneousspatialpointprocessesintensityandconditionalintensityestimation