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Point processes are stochastic models generating interacting points or eventsin time, space, etc. Among characteristics of these models, first-orderintensity and conditional intensity functions are often considered. We focus oninhomogeneous parametric forms of these functions assumed to depend on acertain number of spatial covariates. When this number of covariates is large,we are faced with a high-dimensional problem. This paper provides an overviewof these questions and existing solutions based on regularizations.

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