Online Seminar Talk
Functional Concurrent Regression with Compositional Covariates to Study the Time-Varying Effect of Causes of Death on Human Longevity
Emanuele Giovanni Depaoli
Laboratory of Digital and Computational Demography, April 12, 2022
We study the relationship over time between causes of death and longevity.
Specifically, we investigate whether variations in cause-specific mortality rates can be predictive of life expectancy at birth, considering three compositions of causes of death related to three age classes, namely 0–4, 40–64 and 65+. Employing tools from functional and compositional data analysis, we propose a novel functional concurrent regression model with compositional covariates. A penalized estimation procedure for estimating the regression coefficients and for selecting variables is developed. The results confirm the important role of neoplasms and cardiovascular diseases emerged in other studies and reveal other contributions not observed yet.
Emanuele is a PhD Student in Statistical Sciences at the University of Padova, currently visiting the MPIDR Laboratory of Digital and Computational Demography. He received his master's degree in Statistical Sciences from the University of Padova in 2020. His research focuses on developing cutting-edge statistical methods for demographic and social data with complex structures, mortality modelling and forecasting.