Aggregate loss model with Poisson-Tweedie frequency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<abstract> Aggregate loss models are used by insurers to make operational decisions, set insurance premiums, optimize reinsurance and manage risk. The aggregate loss is the summation of all random losses that occurred in a period, and it is a function of both the loss severity and the loss frequency. The need for a flexible model in fitting severity has been well studied in the literature. We extend this work by introducing the Poisson-Tweedie distribution family for the frequency distribution. The Poisson-Tweedie distribution family contains many of the commonly used distributions for modelling loss frequency, thus making loss frequency fitting more flexible and reducing the chance of model misspecification. Using simulation, we show that the sensitivity of percentile based risk measures to different specifications of the frequency distribution. We then apply our proposed model to the Transportation Security Administration (TSA) claims data to demonstrate modelling capacity of the Poisson-Tweedie distribution. </abstract>
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it