New Robust Estimators for the Nonparametric Regression Model: Application and Simulation Study
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
This paper introduces new two robust kernel-based estimators (S Kernel and MM Kernel) for the nonparametric regression mode in the presence of outliers. Through comprehensive simulations, we evaluate their performance using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Relative Efficiency (RE) under varying sample sizes and outlier contamination levels. Results demonstrate that robust estimators consistently outperform traditional kernel estimator, delivering the lowest estimation errors and highest efficiency, particularly in high-contamination scenarios. In contrast, the traditional kernel estimator proves highly sensitive to outliers. Also, our results highlight the superiority of the robust M Kernel estimator. This paper advances the field of robust nonparametric regression, offering practical solutions for datasets prone to outliers.
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 | 0.000 |
| 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