Direkt zur Hauptnavigation springen Direkt zum Inhalt springen
info(at)avldigital.remove-this.de
Deutsch English
Letzte Suchanfragen

Ergebnisse für *

Zeige Ergebnisse 1 bis 5 von 5.

  1. Identifying and correcting bias in big crowd-sourced online genealogies
    Autor*in: Chong, Michael; Alburez-Gutierrez, Diego; Del Fava, Emanuele; Alexander, Monica; Zagheni, Emilio
    Erschienen: [2022]
    Verlag:  Max Planck Institute for Demographic Research, Rostock, Germany

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

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 473
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: MPIDR working paper ; WP 2022, 005 (January 2022)
    Schlagworte: Denmark; Finland; France; Norway; Sweden; USA; bias; genealogy; mortality; statistics
    Umfang: 1 Online-Ressource (circa 35 Seiten), Illustrationen
  2. Predicting individual-level longevity with statistical and machine learning methods
    Autor*in: Badolato, Luca; Decter-Frain, Ari; Irons, Nicholas J.; Miranda, Maria; Walk, Erin; Zhalieva, Elnura; Alexander, Monica; Basellini, Ugofilippo; Zagheni, Emilio
    Erschienen: [2023]
    Verlag:  Max Planck Institute for Demographic Research, Rostock, Germany

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

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 473
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: MPIDR working paper ; WP 2023, 008 (February 2023)
    Schlagworte: USA; forecasts; inequality; longevity
    Umfang: 1 Online-Ressource (circa 27 Seiten), Illustrationen
  3. The limits of predicting individual-level longevity
    Autor*in: Badolato, Luca; Decter-Frain, Ari; Irons, Nicholas J.; Miranda, Maria; Walk, Erin; Zhalieva, Elnura; Alexander, Monica; Basellini, Ugofilippo; Zagheni, Emilio
    Erschienen: 2023
    Verlag:  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... mehr

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

    Standort:
    Deutsches Zentrum für Altersfragen e.V. (DZA), Bibliothek
    Fernleihe:
    uneingeschränkte Fernleihe, Kopie und Ausleihe

     

    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 in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: MPIDR Working Paper ; 2009-004
    Umfang: 1 Online-Ressource (circa 32 Seiten)
  4. 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
    Autor*in: Pojman, Elena Maria; Mwedzi, Duke; Bucaro, Orlando Olaya; Zhang, Stephanie; Chong, Michael Y. C.; Alexander, Monica; Alburez-Gutierrez, Diego
    Erschienen: [2023]
    Verlag:  Max Planck Institute for Demographic Research, Rostock, Germany

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

    Standort:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signatur:
    VS 473
    Fernleihe:
    keine Fernleihe
    Export in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Weitere Identifier:
    Schriftenreihe: MPIDR working paper ; WP 2023, 050 (December 2023)
    Schlagworte: crowd-sourced genealogies; migrant mortality advantage; United Kingdom; Ireland; sibling effects
    Umfang: 1 Online-Ressource (circa 45 Seiten), Illustrationen
  5. The limits of predicting individual-level longevity
    Autor*in: Badolato, Luca; Decter-Frain, Ari; Irons, Nicholas J.; Miranda, Maria; Walk, Erin; Zhalieva, Elnura; Alexander, Monica; Basellini, Ugofilippo; Zagheni, Emilio
    Erschienen: 2023
    Verlag:  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... mehr

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

    Standort:
    Deutsches Zentrum für Altersfragen e.V. (DZA), Bibliothek
    Fernleihe:
    keine Fernleihe

     

    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 in Literaturverwaltung   RIS-Format
      BibTeX-Format
    Quelle: Verbundkataloge
    Sprache: Englisch
    Medientyp: Buch (Monographie)
    Format: Online
    Schriftenreihe: MPIDR Working Paper ; 2009-004
    Umfang: 1 Online-Ressource (circa 32 Seiten)