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  4. Mortality Modeling: Machine Learning and Mortality Shocks
 
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2022
Doctoral Thesis
Titel

Mortality Modeling: Machine Learning and Mortality Shocks

Abstract
This thesis offers a thorough review of stochastic mortality models, including various calibration and forecasting procedures. As a new contribution to the literature, machine learning methods are applied, yielding fresh perspectives on mortality data and improved forecasting performance. Benefits of classical models such as interpretability and prediction uncertainty quantification are preserved as much as possible or even enhanced.
ThesisNote
Zugl.: Kaiserslautern, TU, Diss., 2022
Author(s)
Schnürch, Simon
Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Verlag
Fraunhofer Verlag
DOI
10.24406/publica-192
File(s)
1831-8_Schnuerch_ePrint.pdf (14.16 MB)
Language
English
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Fraunhofer-Institut für Techno- und Wirtschaftsmathematik ITWM
Tags
  • Mortality modeling

  • Machine learning

  • Mortality shocks

  • Lee-Carter model

  • Neural networks

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