Year: 2026
Type: article
Abstract: Many studies collect data that can be considered as a realization of a point process. Partial least squares (PLS) is a popular analytic approach that combines features from linear modeling as well as dimension reduction to provide parsimonious prediction and classification. However, existing PLS methodologies do not include the analysis of point process predictors. In this article, we introduce point process PLS ($P^3LS$) for analyzing latent time-varying intensity functions from collections of inhomogeneous point processes. We develop a novel estimation procedure for $P^3LS$ that utilizes the properties of log-Gaussian Cox processes and examine its empirical properties via simulation studies. In addition, we establish the predictive consistency for the $P^3LS$. Finally, we apply the proposed method to a renal radionuclide imaging study to predict kidney obstruction from patient renograms. The method yields a clinically meaningful and interpretable model, demonstrating its potential to support data-driven clinical decision-making in renal studies.
Source: Biometrics
Institution: Emory University
Language: English
Subfield: Applied Mathematics
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Developing statistical image analysis tools for non-invasive monitoring of anemia in low birth weight infants, Adaptive Frequency Band Estimation and Analysis, Enhancing prefrontal oscillatory activity and working memory performance with noninvasive brain stimulation in early-course schizophrenia
OpenAlex ID: W4405469805