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: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ESAIM: Proceedings and Surveys |
| Online Access: | https://www.esaim-proc.org/articles/proc/pdf/2025/03/proc20258001.pdf |
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| Summary: | 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 |