Time Series of Correlated Count Data usingMultifractal Process
Bibliographic record
Abstract
This paper generalizes Poisson-Multifractal for correlated time series of count data. We show that the model has useful properties; it captures long-term time dependence and exible dependence between types of count. Based on real data, the correlated multifractal model is used to model the number of claims of two separate coverages in automobile insurance. Smoothed values of the underlying process can be estimated, and a speci c property of the model allows us to split the unobserved process into separate elements. These elements can be considered as climatic, economic or social factors a ecting the frequency of claims, which can be associated with exogeneous informations. Even if the model proposed in this paper implies dependence between count variables, we think that it can be easily generalized in many directions: to model dependence between claim cost and frequency, or between the claims frequency of di erent insurance products.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".