Variable selection for inhomogeneous spatial point process models

Document Type

Article

Publication Date

6-1-2015

Abstract

In this work, we consider variable selection when modelling the intensity and clustering of inhomogeneous spatial point processes, integrating well-known procedures in the respective fields of variable selection and spatial point process modelling to introduce a simple procedure for variable selection in spatial point process modelling. Specifically, we consider modelling spatial point data with Poisson, pairwise interaction and Neyman-Scott cluster models, and incorporate LASSO, adaptive LASSO, and 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, modelling 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.

Identifier

84929605921 (Scopus)

Publication Title

Canadian Journal of Statistics

External Full Text Location

https://doi.org/10.1002/cjs.11244

e-ISSN

1708945X

ISSN

03195724

First Page

288

Last Page

305

Issue

2

Volume

43

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