Regularization techniques for inhomogeneous (spatial) point processes intensity and conditional intensity estimation
arXiv:2305.13470
Abstract
Point processes are stochastic models generating interacting points or events in time, space, etc. 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.
arXiv comment19 pages, 2 figures
References in corpus (4)
- On the adaptive elastic-net with a diverging number of parameters
- Determinantal point process models and statistical inference : Extended version
- Quasi-likelihood for Spatial Point Processes
- Asymptotic properties of the maximum pseudo-likelihood estimator for stationary Gibbs point processes including the Lennard-Jones model