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Displaying results 1 to 5 of 5.

  1. Predicting individual-level longevity with statistical and machine learning methods
    Author: Badolato, Luca; Decter-Frain, Ari; Irons, Nicholas J.; Miranda, Maria; Walk, Erin; Zhalieva, Elnura; Alexander, Monica; Basellini, Ugofilippo; Zagheni, Emilio
    Published: [2023]
    Publisher:  Max Planck Institute for Demographic Research, Rostock, Germany

    Access:
    Verlag (kostenfrei)
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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    VS 473
    Inter-library loan:
    No inter-library loan
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    Series: MPIDR working paper ; WP 2023, 008 (February 2023)
    Subjects: USA; forecasts; inequality; longevity
    Scope: 1 Online-Ressource (circa 27 Seiten), Illustrationen
  2. Identifying and correcting bias in big crowd-sourced online genealogies
    Author: Chong, Michael; Alburez-Gutierrez, Diego; Del Fava, Emanuele; Alexander, Monica; Zagheni, Emilio
    Published: [2022]
    Publisher:  Max Planck Institute for Demographic Research, Rostock, Germany

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    VS 473
    Inter-library loan:
    No inter-library loan
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    Series: MPIDR working paper ; WP 2022, 005 (January 2022)
    Subjects: Denmark; Finland; France; Norway; Sweden; USA; bias; genealogy; mortality; statistics
    Scope: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  3. Leaving for life
    using online crowd- sourced genealogies to estimate the migrant mortality advantage for the United Kingdom and Ireland during the 18th and 19th centuries
    Author: Pojman, Elena Maria; Mwedzi, Duke; Bucaro, Orlando Olaya; Zhang, Stephanie; Chong, Michael Y. C.; Alexander, Monica; Alburez-Gutierrez, Diego
    Published: [2023]
    Publisher:  Max Planck Institute for Demographic Research, Rostock, Germany

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)
    Verlag (kostenfrei)

    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    VS 473
    Inter-library loan:
    No inter-library loan
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    Series: MPIDR working paper ; WP 2023, 050 (December 2023)
    Subjects: crowd-sourced genealogies; migrant mortality advantage; United Kingdom; Ireland; sibling effects
    Scope: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  4. The limits of predicting individual-level longevity
    Author: Badolato, Luca; Decter-Frain, Ari; Irons, Nicholas J.; Miranda, Maria; Walk, Erin; Zhalieva, Elnura; Alexander, Monica; Basellini, Ugofilippo; Zagheni, Emilio
    Published: 2023
    Publisher:  Max-Planck-Inst. für demografische Forschung, Rostock

    Individual-level mortality prediction is a fundamental challenge with implications for life planning, social policies and public spending. We model and predict individual-level lifespan using 12 traditional and state-of-the-art models and over 150... more

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    Deutsches Zentrum für Altersfragen e.V. (DZA), Bibliothek
    Inter-library loan:
    Unlimited inter-library loan, copies and loan

     

    Individual-level mortality prediction is a fundamental challenge with implications for life planning, social policies and public spending. We model and predict individual-level lifespan using 12 traditional and state-of-the-art models and over 150 predictors derived from the U.S. Health and Retirement Study. Machine learning and statistical models report comparable accuracy and relatively high discriminative performance, but fail to account for most lifespan heterogeneity at the individual level. We observe consistent inequalities in mortality predictability and risk discrimination, with lower accuracy for men, non-Hispanic Blacks, and low-educated individuals. Additionally, people in these groups show lower accuracy in their subjective predictions of their own lifespan. Finally, top features across groups are similar, with variables related to habits, health history, and finances being relevant predictors. We conclude by highlighting the limits of predicting mortality from representative surveys and the inequalities across social groups, providing baselines and guidance for future research and public policies.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: MPIDR Working Paper ; 2009-004
    Scope: 1 Online-Ressource (circa 32 Seiten)
  5. The limits of predicting individual-level longevity
    Author: Badolato, Luca; Decter-Frain, Ari; Irons, Nicholas J.; Miranda, Maria; Walk, Erin; Zhalieva, Elnura; Alexander, Monica; Basellini, Ugofilippo; Zagheni, Emilio
    Published: 2023
    Publisher:  Max-Planck-Inst. für demografische Forschung, Rostock

    Individual-level mortality prediction is a fundamental challenge with implications for life planning, social policies and public spending. We model and predict individual-level lifespan using 12 traditional and state-of-the-art models and over 150... more

    Access:
    Verlag (kostenfrei)
    Resolving-System (kostenfrei)

    Location:
    Deutsches Zentrum für Altersfragen e.V. (DZA), Bibliothek
    Inter-library loan:
    No inter-library loan

     

    Individual-level mortality prediction is a fundamental challenge with implications for life planning, social policies and public spending. We model and predict individual-level lifespan using 12 traditional and state-of-the-art models and over 150 predictors derived from the U.S. Health and Retirement Study. Machine learning and statistical models report comparable accuracy and relatively high discriminative performance, but fail to account for most lifespan heterogeneity at the individual level. We observe consistent inequalities in mortality predictability and risk discrimination, with lower accuracy for men, non-Hispanic Blacks, and low-educated individuals. Additionally, people in these groups show lower accuracy in their subjective predictions of their own lifespan. Finally, top features across groups are similar, with variables related to habits, health history, and finances being relevant predictors. We conclude by highlighting the limits of predicting mortality from representative surveys and the inequalities across social groups, providing baselines and guidance for future research and public policies.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: MPIDR Working Paper ; 2009-004
    Scope: 1 Online-Ressource (circa 32 Seiten)