Fixed Point Maximum Likelihood Estimation for the Epanechnikov-Pareto Distribution
Bibliographic record
Abstract
This paper develops a fixed-point iteration method for maximum likelihood estimation of the shape parameter θ in the Epanechnikov-Pareto Distribution (EPD). Building on Banach’s contraction principle, we establish a computationally efficient algorithm that reformulates the MLE problem as a fixed-point equation. Numerical simulations demonstrate rapid convergence within 6-10 iterations, reducing geometric error from 0.325 to 4.04×10−7. The proposed method significantly outperforms conventional optimization techniques, requiring only 18 iterations compared to 145 for Nelder-Mead while maintaining equivalent accuracy. Bootstrap validation with 500 replications confirms estimator stability, yielding a narrow 95% confidence interval [0.324015, 0.340532] with standard deviation 0.004148. The fixed-point approach provides a robust framework for parameter estimation in heavy-tailed distributions, with applications in reliability engineering and financial modeling.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; a candidate call from one source (direct Gemma or distilled Codex), 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".