Variable selection for inhomogeneous spatial point process models

Yu Ryan Yue, Ji Meng Loh
2015 Canadian journal of statistics  
In this work, we consider variable selection when modeling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modeling to introduce a simple procedure for variable selection in spatial point process modeling. Specifically, we consider modeling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO and
more » ... elastic net regularization methods into the generalized linear model framework for fitting these point models. We perform simulation studies to explore the effectiveness of using each of the three regularization methods in our procedure. We then use the procedure in two applications, modeling the intensity and clustering of rainforest trees with soil and geographical covariates using a Neyman-Scott model, and of fast food restaurant locations in New York City with Census variables and school locations using a pairwise interaction model.
doi:10.1002/cjs.11244 fatcat:3yqkbgerabcjtie6ymmr74slw4