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

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Title: Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation.
Authors: Coeurjolly, Jean-Francois1 (AUTHOR), Ba, Ismaïla2 (AUTHOR), Choiruddin, Achmad3 (AUTHOR)
Source: ESAIM: Proceedings & Surveys. 2025, Vol. 80, p2-16. 15p.
Subjects: Point processes, Stochastic models, Stochastic processes
Abstract (English): 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. [ABSTRACT FROM AUTHOR]
Abstract (French): Les processus ponctuels constituent une classe de modèles stochastiques permettant de modéliser des évènements dans le temps et/ou l'espace en interaction. Parmi les caractéristiques d'un processus ponctuel, l'intensitéet l'intensitéconditionnelle d'ordre un sont souvent considérées. Nous nous concentrons ici sur des formes paramétriques inhomogènes de ces fonctions que nous supposons dépendre d'un certain nombre de covariables spatiales. Lorsque ce nombre est élevé, nous faisons face à un problème de grande dimension. Ce papier a pour objectif de présenter un aperçu de ces problèmes et solutions existantes. [ABSTRACT FROM AUTHOR]
Copyright of ESAIM: Proceedings & Surveys is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation.
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  Data: <searchLink fieldCode="AR" term="%22Coeurjolly%2C+Jean-Francois%22">Coeurjolly, Jean-Francois</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ba%2C+Ismaïla%22">Ba, Ismaïla</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Choiruddin%2C+Achmad%22">Choiruddin, Achmad</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22ESAIM%3A+Proceedings+%26+Surveys%22">ESAIM: Proceedings & Surveys</searchLink>. 2025, Vol. 80, p2-16. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Point+processes%22">Point processes</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+models%22">Stochastic models</searchLink><br /><searchLink fieldCode="DE" term="%22Stochastic+processes%22">Stochastic processes</searchLink>
– Name: Abstract
  Label: Abstract (English)
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label: Abstract (French)
  Group: Ab
  Data: Les processus ponctuels constituent une classe de modèles stochastiques permettant de modéliser des évènements dans le temps et/ou l'espace en interaction. Parmi les caractéristiques d'un processus ponctuel, l'intensitéet l'intensitéconditionnelle d'ordre un sont souvent considérées. Nous nous concentrons ici sur des formes paramétriques inhomogènes de ces fonctions que nous supposons dépendre d'un certain nombre de covariables spatiales. Lorsque ce nombre est élevé, nous faisons face à un problème de grande dimension. Ce papier a pour objectif de présenter un aperçu de ces problèmes et solutions existantes. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of ESAIM: Proceedings & Surveys is the property of EDP Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1051/proc/202580002
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 2
    Subjects:
      – SubjectFull: Point processes
        Type: general
      – SubjectFull: Stochastic models
        Type: general
      – SubjectFull: Stochastic processes
        Type: general
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      – TitleFull: Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation.
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            – D: 01
              M: 09
              Text: 2025
              Type: published
              Y: 2025
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              Value: 80
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