Modélisation de l'espérance de vie des clients en assurance
Bibliographic record
Abstract
Dans ce mémoire, nous proposons une méthodologie statistique permettant\nd’obtenir un estimateur de l’espérance de vie des clients en assurance. Les\nprédictions effectuées tiennent compte des caractéristiques individuelles des\nclients, notamment du fait qu’ils peuvent détenir différents types de produits\nd’assurance (automobile, résidentielle ou les deux). Trois approches sont comparées.\nLa première approche est le modèle de Markov simple, qui suppose à\nla fois l’homogénéité et la stationnarité des probabilités de transition. L’autre\nmodèle – qui a été implémenté par deux approches, soit une approche directe\net une approche par simulations – tient compte de l’hétérogénéité des probabilités\nde transition, ce qui permet d’effectuer des prédictions qui évoluent avec\nles caractéristiques des individus dans le temps. Les probabilités de transition\nde ce modèle sont estimées par des régressions logistiques multinomiales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".