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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Entropy analysis of human death uncertainty.

J A Tenreiro Machado1, António M Lopes2

  • 1Department of Electrical Engineering, Institute of Engineering, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4249 - 015 Porto, Portugal.

Nonlinear Dynamics
|May 31, 2021
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Summary

This study uses entropy measures, including Shannon entropy and cumulative residual entropy, to analyze death uncertainty. Findings reveal distinct mortality dynamics across 40 countries over decades.

Keywords:
Death uncertaintyEntropyHierarchical clusteringLifetableState state portrait

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Area of Science:

  • Demographic and actuarial sciences
  • Statistical analysis of mortality data

Background:

  • Uncertainty surrounding the time of death is a significant factor in demographic and actuarial sciences.
  • Entropy is a valuable concept for characterizing complex systems, including human mortality patterns.

Purpose of the Study:

  • To analyze death uncertainty using entropy measures, specifically Shannon entropy and cumulative residual entropy.
  • To characterize the dynamics of human mortality by examining country-specific and inter-country entropy patterns.

Main Methods:

  • Application of Shannon entropy (as average information) and cumulative residual entropy (related to reliability measures).
  • Analysis of mortality data from the Human Mortality Database, encompassing 40 countries over several decades.
  • Investigation of emerging country and inter-country entropy patterns to understand mortality dynamics.

Main Results:

  • Identification of distinct entropy patterns reflecting the dynamics of mortality across different countries.
  • Characterization of country-specific and inter-country mortality trends through entropy analysis.
  • Demonstration that the interplay of Shannon and cumulative residual entropies provides deeper insights into human mortality data evolution.

Conclusions:

  • Entropy measures offer a robust framework for understanding and quantifying death uncertainty.
  • The study highlights the utility of entropy in characterizing the complex dynamics of human mortality on a global scale.
  • The combined analysis of different entropy measures deepens the understanding of human mortality data evolution over time.