Expressive mortality models through Gaussian process kernels

DOI10.1017/ASB.2023.39zbMATH Open1545.91273MaRDI QIDQ6556603

Michael Ludkovski, J. Risk

Publication date: 17 June 2024

Published in: ASTIN Bulletin (Search for Journal in Brave)

zbMATH Keywords

genetic algorithmsGaussian process kernel engineeringHuman Mortality Databasemortality surfaces

Mathematics Subject Classification ID

Gaussian processes (60G15) Actuarial mathematics (91G05)

Cites Work

• Title not available (Why is that?)
• Title not available (Why is that?)
• Title not available (Why is that?)
• A Poisson log-bilinear regression approach to the construction of projected lifetables.
• Advanced stationary and nonstationary kernel designs for domain-aware Gaussian processes
• Large-scale local surrogate modeling of stochastic simulation experiments
• A cohort-based extension to the Lee-Carter model for mortality reduction factors
• 10.1162/15324430260185646
• An Approach to Time Series Analysis
• GAUSSIAN PROCESS MODELS FOR MORTALITY RATES AND IMPROVEMENT FACTORS
• Time-series forecasting of mortality rates using deep learning
• A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States
• The GLM framework of the Lee–Carter model: a multi-country study
• The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications
• A General Procedure for Constructing Mortality Models

This page was built for publication: Expressive mortality models through Gaussian process kernels

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6556603)