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An R-Based Landscape Validation of a Competing Risk Model

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Sensitivity and uncertainty in the Lee-Carter mortality model.

Wenyun Zuo¹, Anil Damle², Shripad Tuljapurkar¹

¹Department of Biology, Stanford University, Stanford, CA 94305-5020, USA.

International Journal of Forecasting

|April 24, 2025

Summary

The Lee-Carter (LC) model for mortality forecasting is robust. Sensitivity analyses and simulations show it handles data uncertainty and randomness effectively, especially for short-term changes.

Keywords:

Singular value decomposition age vector death rate matrix perturbation theory randomness robustness time vector variance

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Area of Science:

Demography
Actuarial Science
Biostatistics

Background:

The Lee-Carter (LC) model is a standard for forecasting age-specific mortality rates.
Its performance is often considered reliable despite data quality issues and inherent uncertainty.

Purpose of the Study:

To analyze the robustness of the Lee-Carter model.
To investigate the impact of data uncertainty and randomness on LC model performance.

Main Methods:

Utilized matrix perturbation theory for sensitivity analyses.
Employed simulations to assess the effects of random variations in mortality data.
Combined sensitivity and uncertainty analyses to determine model robustness.

Main Results:

LC model sensitivity and death rate uncertainty exhibit non-uniform patterns across ages and years.
Sensitivities are generally low, with peaks at the beginning and end of periods.
High uncertainties in death rates are observed at young (5-19) and old (90+) ages.

Conclusions:

The Lee-Carter model demonstrates robustness against random perturbations in mortality data.
The model is resilient to sudden, short-term changes in mortality patterns.