Evaluation of the forecasting accuracy of stochastic mortality models: An analysis of developed and developing countries
Bibliographic record
Abstract
This paper evaluates the accuracy performance of eight stochastic mortality models in the forecasting of the male mortality rates pertaining to different age groups and countries. The mortality datasets for three developed countries (Canada, France and Japan) and two developing countries (Taiwan and Ukraine) are employed in this study. For each country, the age range is split into three age groups – A (0–19), B (20–60) and C (61–90). The forecasting accuracy of the mortality models is evaluated using the RMSE, MAE, MPE and MAPE metrics. Mortality models with more complex specifications perform better for the age groups B and C, than for the age group A. The cohort feature is more significant for age categories B and C, especially for the developed countries where there are significant medical and health advances. From an overall perspective, the Lee-Carter, Renshaw-Haberman and Age-Period-Cohort models are superior for the age group A while the Plat model proves to be the best forecasting model for the age categories B and C. The empirical analysis concludes that the mortality patterns diverge for different age categories and countries with different development status. The occurrence of extreme mortality events also negatively affects the patterns of human mortality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".