INSURANCE RISK CLASSIFICATION WITH NEGATIVE BINOMIAL DISTRIBUTION
Bibliographic record
Abstract
Risk classification is the process of statistical modeling that classifies risks into cross-classified classes, characterized by the rating factors. In this paper, risk classification is applied to estimate claim frequency rates, expressed in terms of claim count per exposure unit. The Poisson regression model has been widely used to analyze claim frequency rates in the recent years. However, under the Poisson model, the mean and variance is assumed to be equal within classes, i.e., homogeneous rates. In this paper, the Negative Binomial regression model is suggested to deal with heterogeneous rates. In addition, the measures for goodness-of-fit of the model, namely the Pearson chi-square, deviance, and likelihood ratio test, are also discussed. Finally, the procedure for estimation of parameters, namely the Iteratively Weighted Least Squares (IWLS), is also shown. In this paper, the models are fitted and tested on two types of claim data; Canadian private automobile liability insurance and Malaysian private automobile own damage insurance.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".