Work

Exploratory Continuous-Time Modeling (Expct): Extracting Dynamic Features from Irregularly Spaced Time Series

Year: 2026

Type: article

Abstract: model: (1) addresses intensive data across virtually any time scheme, (2) allows for data collected at different time scales to predict themselves and one another, and (3) makes fewer assumptions about the functional forms of the longitudinal system dynamics between variables.

Source: Multivariate Behavioral Research

DOI: 10.1080/00273171.2026.2717741

Authors: Oisín Ryan, Kejin Wu, Nicholas C. Jacobson

Institutions: University Medical Center Utrecht, Loyola University Chicago, Dartmouth College

Language: English

Topic: Statistical Methods and Bayesian Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Institute of Mental Health, National Institute on Drug Abuse

Awards: G4281474698, Technology-based Treatments for Substance Use Disorders