Learning and predicting from dynamic models for COVID-19 patient monitoring

DOI10.1214/22-STS861MaRDI QIDQ2143952

Akihiko Nishimura, Antony Rosen, Mary Grace Bowring, Brian Garibaldi, Zitong Wang, Scott L. Zeger

Publication date: 31 May 2022

Published in: Statistical Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2111.01817

zbMATH Keywords

predictiondecision supportlongitudinal data analysisstatistical graphicsinverse regression

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Data science in a time of crisis: lessons from the pandemic

Cites Work

• Modeling discrete time-to-event data
• Slicing regression: A link-free regression method
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Inverse regression for longitudinal data
• Joint Models for Longitudinal and Time-to-Event Data
• Interpretable Dynamic Treatment Regimes
• Enhanced photoluminescence decay processes of doped CaS phosphors at low temperature
• Selection Models and Pattern‐Mixture Models for Incomplete Data with Covariates
• Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
• Pattern-Mixture Models for Multivariate Incomplete Data
• Joint Models for Multivariate Longitudinal and Multivariate Survival Data

This page was built for publication: Learning and predicting from dynamic models for COVID-19 patient monitoring