2023/12/15 by Connor Gascoigne, Gascoigne, Connor, Theresa J. Smith +3
Decision Sciences · Health Professions · Social Sciences · #FOS: Computer and information sciences #Global Health Care Issues #Insurance, Mortality, Demography, Risk Management #Methodology (stat.ME) #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.2312.09698
openalex publication_date 2023/12/15 · openalex created_date 2023/12/19 · openalex updated_date 2026/07/28
Age-Period-Cohort (APC) models are well used in the context of modelling health and demographic data to produce smooth estimates of each time trend. When smoothing in the context of APC models, there are two main schools, frequentist using penalised smoothing splines, and Bayesian using random processes with little crossover between them. In this article, we clearly lay out the theoretical link between the two schools, provide examples using simulated and real data to highlight similarities and difference, and help a general APC user understand potentially inaccessible theory from functional analysis. As intuition suggests, both approaches lead to comparable and almost identical in-sample predictions, but random processes within a Bayesian approach might be beneficial for out-of-sample prediction as the sources of uncertainty are captured in a more complete way.