Bibliographic record
Abstract
This thesis describes a model for predicting individual claim losses and estimating the capital reserve for the automobile portfolio of a large Canadian insurance company. Depending on the nature of a claim, its settlement can involve medical costs, rehabilitation costs, income compensation costs, optional coverage costs, and even death benefit coverage. Any combination of these costs can occur, and the dependence between them must be accounted for. To this end, a two-level hierarchical structure is adopted. First, a multinomial logistic model is used to predict the combination of costs associated to a claim. The claim severity is then modeled as a function of this composition. A Log-Normal model is used to predict different types of loss; claimant information, accident information, medical and legal report information serve as explanatory variables. The dependence between medical, rehabilitation and income loss is characterized by a Gumbel copula. A Bayesian framework with Markov Chain Monte Carlo sampling is adopted to estimate jointly the copula regression model parameters. Simulations are carried out to obtain prediction of individual loss, the distribution of total portfolio loss and the capital reserve.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".