Arbeitsbereich

Bevölkerungsdynamik und Nachhaltiges Wohlbefinden

Auf einen Blick Projekte Publikationen Team

Projekt

Forecasting Individual-Level Mortality

Ugofilippo Basellini, Emilio Zagheni, Monica Alexander (University of Toronto, Kanada), Luca Badolato (The Ohio State University, Columbus, Vereinigte Staaten), Ari Gabriel Decter-Frain (Cornell University, Ithaca, Vereinigte Staaten), Nicolas Irons (University of Washington, Seattle, Vereinigte Staaten), Maria Laura Miranda (MPIDR / Federal University of Minas Gerais, Belo Horizonte, Brasilien), Erin Walk (MPIDR / Massachusetts Institute of Technology, Cambridge, Vereinigte Staaten), Elnura Zhalieva (Mohamed bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, Vereinigte Arabische Emirate)

In this project, we provide novel insights into the predictability of individual lifespans, employing both classic statistical models and emerging machine-learning approaches. We focus on prediction accuracy and inequalities in predictability across socioeconomic groups.  Ausführliche Beschreibung

Prediction accuracy of individual-level lifespan for different models by gender, race and ethnicity, and education

Integrated Brier Score and Mean Area Under the Curve by gender (panel a.), race and ethnicity (panel b.), and education (panel c.). Prediction accuracy across models is worse for Men, non-Hispanic Blacks and low-educated individuals as compared to other socioeconomic groups. © © Badolato et al. (2023)

Publikationen

Badolato, L.; Decter-Frain, A. G.; Irons, N.; Miranda, M. L.; Walk, E.; Zhalieva, E.; Alexander, M.; Basellini, U.; Zagheni, E.:

Predicting individual-level longevity with statistical and machine learning methods

MPIDR Working Paper WP-2023-008. (2023)