On the convoluted gamma to length-biased inverse Gaussian distribution and application in financial modeling
Why this work is in the frame
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Bibliographic record
Abstract
This paper studies a convoluted form of length-biased inverse Gaussian and gamma distributions due to its structural relationship with the Wright distribution [Naik and Abraham 2013]. The convoluted form of the derived distribution is named as Inverse Gaussian-gamma abbreviated as IGG distribution which shows heavy-tailedness properties and unimodality. The study also examines some interesting statistical properties of the distribution and compares them with inverse Gaussian and gamma distributions. Results show that the IGG model outperformed inverse Gaussian and gamma distributions through its model characteristics. A theoretical application of the IGG distribution is established to illustrate the model applicability in the financial industry that explains the versatility of the distribution in data analysis. Despite these applications, an autoregressive model of order one is derived to establish utilization of the distribution in time series modeling.
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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 | 0.001 |
| 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 | 0.000 |
| 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