Bühlmann Credibility-Based Approaches to Modeling Mortality Rates for Multiple Populations
Bibliographic record
Abstract
Inspired by the ideas of the joint-k, the co-integrated, the common factor, and the augmented common factor Lee-Carter models, in this article, we propose four corresponding Bühlmann credibility-based mortality models for multiple populations. Our models and the four Lee-Carter models are fitted with mortality data from the Human Mortality Database for both genders of the United States, the United Kingdom, and Japan to forecast mortality rates for three forecasting periods. Based on the measure of AMAPE (average of mean absolute percentage error), numerical illustrations show that our Bühlmann credibility-based models contribute to more accurate forecasts than the Lee-Carter-based models in all three forecasting periods. Finally, we also propose a stochastic version of the multi-population Bühlmann credibility-based mortality models, which can be used to construct predictive intervals on the projected mortality rates and to conduct stochastic simulations for applications.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".