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Multipopulation mortality modelling and forecasting: the weighted multivariate functional principal component

Ka Kin Lam1, Bo Wang1

  • 1School of Mathematics and Actuarial Science, University of Leicester, Leicester, UK.

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PubMed
Summary
This summary is machine-generated.

This study introduces two new models for joint mortality forecasting in multiple subpopulations. The second model, using multivariate functional principal component analysis, significantly improves forecast accuracy over existing methods.

Keywords:
Lee–Carter modelMortality modellingcoherent forecastsfunctional principal component analysismultivariate functional data analysisproduct-ratio model

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Area of Science:

  • Demography
  • Biostatistics
  • Statistical modeling

Background:

  • Human mortality patterns across related populations often exhibit similarities.
  • Simultaneous modeling of multiple subpopulations is desirable but challenging due to heterogeneity.
  • Existing methods may not fully capture the coherent evolution of mortality in linked groups.

Purpose of the Study:

  • To introduce novel models for joint mortality modeling and forecasting across multiple subpopulations.
  • To develop a multivariate functional principal component analysis (MFPCA) approach for coherent mortality modeling.
  • To compare the performance of new models against established methods using real-world mortality data.

Main Methods:

  • Extension of the independent functional data model to a multipopulation setting.
  • Development of a novel multivariate functional principal component method for coherent modeling.
  • Application and comparison using sex-specific mortality data from ten developed countries.

Main Results:

  • The first proposed model demonstrates comparable forecast ability to existing methods.
  • The second proposed model, based on MFPCA, significantly outperforms the first model and existing methods in forecast accuracy.
  • The novel MFPCA approach effectively models the non-diverging evolution of mortality in related subpopulations.

Conclusions:

  • The proposed multivariate functional principal component method offers a superior approach for joint mortality forecasting.
  • Accurate mortality forecasting is crucial for public health, policy-making, and actuarial science.
  • The study highlights the importance of considering interdependencies and common characteristics when modeling mortality across populations.