Generalized point process additive models
Authors
Paper Information
-
Journal:
Journal of the Royal Statistical Society Series B -
DOI:
10.1093/jrsssb/qkag061 -
Published:
April 13, 2026 -
Added to Tracker:
Apr 15, 2026
Abstract
Abstract In this article, we propose a generalized point process additive model with a scalar response and high-dimensional point process predictors. Our proposal is built upon four key components: a realization of a point process as a random counting measure, a generalized point process regression framework, a new kernel function for random measure through kernel embedding, and a suite of low-dimensional structures including the additive model, reduced basis representation, and sparsity. We develop an efficient penalized likelihood procedure for model estimation, and establish both the estimation consistency and selection consistency of the estimator, while allowing the number of point process predictors to diverge. We illustrate and evaluate our method through simulations and an electronic health record data application.
Author Details
Bing Li
AuthorKuang-Yao Lee
AuthorJiehuan Sun
AuthorLexin Li
AuthorCitation Information
APA Format
Bing Li
,
Kuang-Yao Lee
,
Jiehuan Sun
&
Lexin Li
(2026)
.
Generalized point process additive models.
Journal of the Royal Statistical Society Series B
, 10.1093/jrsssb/qkag061.
BibTeX Format
@article{paper1110,
title = { Generalized point process additive models },
author = {
Bing Li
and Kuang-Yao Lee
and Jiehuan Sun
and Lexin Li
},
journal = { Journal of the Royal Statistical Society Series B },
year = { 2026 },
doi = { 10.1093/jrsssb/qkag061 },
url = { https://doi.org/10.1093/jrsssb/qkag061 }
}