Cross generational comparison of stochastic mortality of coupled lives
Bibliographic record
Abstract
This paper studies the evolution across generations of dependence among individuals of a couple.We consider a well-known data set of couples of individuals provided by a large Canadian insurer, and select three di¤erent generations of couples.For each of them, we model the marginal survival functions with a doubly stochastic approach and perform a best-…t selection of the copula.Since the e¤ect of censoring on the dependence structure of the couple varies across di¤erent generations, in the best-…t copula test it is necessary to restrict the attention to the subset of complete data.Despite the small sample available, the remarkable result is that the Kendall's tau varies between 30% for the young generation and 45% for the old one.As a consequence, for every candidate copula the dependence parameter decreases when younger generations are taken into account.This result is intuitive and in accordance with the observed increase in the rate of divorces, the creation of enlarged families and so on.The best …t copula is not invariant across generations, but di¤erent Archimedean copulas perform similarly.An actuarial application to pricing and reserving of joint life products and reversionary annuities shows that not only insurance companies should dismiss the simplifying independence assumption, but they should also select di¤erent dependence parameters for di¤erent generations.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".