Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing a New Framework to Review and Standardise Documentation of Model Assumptions and Facilitate Model Comparison

DOI10.1111/insr.12491arXiv2102.10020OpenAlexW4220838831WikidataQ114627201 ScholiaQ114627201MaRDI QIDQ6067593

Unnamed Author, Leontine Alkema, Unnamed Author

Publication date: 14 December 2023

Published in: International Statistical Review (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2102.10020

zbMATH Keywords

Gaussian processesforecastingBayesian methodshierarchical modelstemporal modelsARIMA processesmodel documentationtemporal models for multiple populations

Mathematics Subject Classification ID

Statistics (62-XX) Inference from stochastic processes (62Mxx) Nonparametric inference (62Gxx)

Cites Work

• Modeling and Forecasting U.S. Mortality
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• A Bayesian approach to the global estimation of maternal mortality
• Global estimation of child mortality using a Bayesian B-spline bias-reduction model
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Measurement Error in Nonlinear Models
• The Analysis of Time Series

This page was built for publication: Temporal Models for Demographic and Global Health Outcomes in Multiple Populations: Introducing a New Framework to Review and Standardise Documentation of Model Assumptions and Facilitate Model Comparison