Gaussian Process Methods for Covariate-Based Intensity Estimation

Fuente: arXiv
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Main Authors: Dolmeta, Patric, Giordano, Matteo
Format: Preprint
Published: 2025
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author Dolmeta, Patric
Giordano, Matteo
author_facet Dolmeta, Patric
Giordano, Matteo
contents We study nonparametric Bayesian inference for the intensity function of a covariate-driven point process. We extend recent results from the literature, showing that a wide class of Gaussian priors, combined with flexible link functions, achieve minimax optimal posterior contraction rates. Our result includes widespread prior choices such as the popular Matérn processes, with the standard exponential (and sigmoid) link, and implies that the resulting methodologically attractive procedures optimally solve the statistical problem at hand, in the increasing domain asymptotics and under the common assumption in spatial statistics that the covariates are stationary and ergodic.
format Preprint
id arxiv_https___arxiv_org_abs_2505_20157
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Gaussian Process Methods for Covariate-Based Intensity Estimation
Dolmeta, Patric
Giordano, Matteo
Statistics Theory
We study nonparametric Bayesian inference for the intensity function of a covariate-driven point process. We extend recent results from the literature, showing that a wide class of Gaussian priors, combined with flexible link functions, achieve minimax optimal posterior contraction rates. Our result includes widespread prior choices such as the popular Matérn processes, with the standard exponential (and sigmoid) link, and implies that the resulting methodologically attractive procedures optimally solve the statistical problem at hand, in the increasing domain asymptotics and under the common assumption in spatial statistics that the covariates are stationary and ergodic.
title Gaussian Process Methods for Covariate-Based Intensity Estimation
topic Statistics Theory
url https://arxiv.org/abs/2505.20157