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GAUSSIAN PROCESS MODELS FOR MORTALITY RATES AND IMPROVEMENT FACTORS.

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Τίτλος: GAUSSIAN PROCESS MODELS FOR MORTALITY RATES AND IMPROVEMENT FACTORS.
Συγγραφείς: Ludkovski, Mike1 ludkovski@pstat.ucsb.edu, Risk, Jimmy2 jrisk@cpp.edu, Zail, Howard3 hzail@elucidor.com
Πηγή: Astin Bulletin. Sep2018, Vol. 48 Issue 3, p1307-1347. 41p.
Θεματικοί όροι: *Mortality -- Mathematical models, *Actuaries, Gaussian processes, Death rate
Εταιρία/Οντότητα: Society of Actuaries
Περίληψη: We develop a Gaussian process (GP) framework for modeling mortality rates and mortality improvement factors. GP regression is a nonparametric, data-driven approach for determining the spatial dependence in mortality rates and jointly smoothing raw rates across dimensions, such as calendar year and age. The GP model quantifies uncertainty associated with smoothed historical experience and generates full stochastic trajectories for out-of-sample forecasts. Our framework is well suited for updating projections when newly available data arrives, and for dealing with "edge" issues where credibility is lower. We present a detailed analysis of GP model performance for US mortality experience based on the CDC (Center for Disease Control) datasets. We investigate the interaction between mean and residual modeling, Bayesian and non-Bayesian GP methodologies, accuracy of in-sample and out-of-sample forecasting, and stability of model parameters. We also document the general decline, along with strong age-dependency, in mortality improvement factors over the past few years, contrasting our findings with the Society of Actuaries (SOA) MP-2014 and -2015 models that do not fully reflect these recent trends. [ABSTRACT FROM AUTHOR]
Copyright of Astin Bulletin is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: We develop a Gaussian process (GP) framework for modeling mortality rates and mortality improvement factors. GP regression is a nonparametric, data-driven approach for determining the spatial dependence in mortality rates and jointly smoothing raw rates across dimensions, such as calendar year and age. The GP model quantifies uncertainty associated with smoothed historical experience and generates full stochastic trajectories for out-of-sample forecasts. Our framework is well suited for updating projections when newly available data arrives, and for dealing with "edge" issues where credibility is lower. We present a detailed analysis of GP model performance for US mortality experience based on the CDC (Center for Disease Control) datasets. We investigate the interaction between mean and residual modeling, Bayesian and non-Bayesian GP methodologies, accuracy of in-sample and out-of-sample forecasting, and stability of model parameters. We also document the general decline, along with strong age-dependency, in mortality improvement factors over the past few years, contrasting our findings with the Society of Actuaries (SOA) MP-2014 and -2015 models that do not fully reflect these recent trends. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Astin Bulletin is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1017/asb.2018.24
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 41
        StartPage: 1307
    Subjects:
      – SubjectFull: Mortality -- Mathematical models
        Type: general
      – SubjectFull: Actuaries
        Type: general
      – SubjectFull: Gaussian processes
        Type: general
      – SubjectFull: Death rate
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      – SubjectFull: Society of Actuaries
        Type: general
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      – TitleFull: GAUSSIAN PROCESS MODELS FOR MORTALITY RATES AND IMPROVEMENT FACTORS.
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            – D: 01
              M: 09
              Text: Sep2018
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              Y: 2018
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