Multi-Peril Frequency Credibility Premium via Shared Random Effects
Bibliographic record
Abstract
It is not uncommon to observe that an insurance policy consists of multiple coverages that cover different perils over multiple years. In this article, we propose a shared random effects model to not only capture the unobserved heterogeneity in risks but also induce a natural dependence structure among the claims from multiple perils. The proposed model has a nice interpretation and comes with closed forms of joint likelihood and credibility premiums that are desirable for ease of implementation in practice. Both the proposed method and benchmark methods are calibrated using a public insurance dataset provided by the Wisconsin Local Government Property Insurance Fund, where one can observe both policy characteristics and historical claims information. It turns out that the proposed method shows acceptable level of performance compared to many pre-existing benchmarks in terms of out-of-sample validation. Address for Correspondence: himchan_jeong@sfu.ca
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".