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Bivariate discrete beta Kernel graduation of mortality data.

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This study introduces a new bivariate discrete beta kernel smoother for mortality data graduation. This advanced method improves accuracy compared to existing univariate and bivariate techniques, especially with exposure data.

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Area of Science:

  • Demography
  • Actuarial Science
  • Statistical Modeling

Background:

  • Graduating mortality data often uses parametric/nonparametric methods.
  • Nonparametric kernel smoothing regression is favored for its flexibility.
  • The univariate discrete beta kernel smoother offers advantages over other methods.

Purpose of the Study:

  • Generalize the discrete beta kernel smoother to the bivariate case.
  • Introduce an adaptive bandwidth variant for improved performance.
  • Outline a cross-validation procedure for bandwidth selection.

Main Methods:

  • Developed a bivariate discrete beta kernel smoother.
  • Incorporated an adaptive bandwidth selection mechanism.
  • Utilized simulation studies with a US male mortality dataset.

Main Results:

  • The proposed bivariate approach demonstrated superior performance.
  • Outperformed both univariate and existing bivariate nonparametric methods.
  • Adaptive bandwidths showed additional benefits with exposure data.

Conclusions:

  • The new bivariate discrete beta kernel smoother is a valuable tool for mortality data graduation.
  • It offers enhanced accuracy and flexibility in demographic and actuarial analyses.
  • The adaptive bandwidth variant holds significant promise for future research.