A Correlation-Driven Adaptive Lasso for Robust Logistic Regression Model Using Trimming Step
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Bibliographic record
Abstract
The presence of contamination can influence the performance of parameter estimation in the binary logistic regression. Additionally, the emergence of collinearity among independent variables also gives rise to the issue of multicollinearity. In this work, we propose a novel correlation-driven adaptive lasso algorithm designed to enhance the robustness of logistic regression by incorporating a trimming step. The efficacy of this approach stems from the synergistic utilization of correlation-driven trimming techniques, which collectively serve to mitigate the impact of contaminated observations. The algorithm is designed to select information highly correlated features adaptively and to detect outilers simultaneously by maximizing a trimmed likelihood function. The proposed method has been evaluated and compared with other exisitng methods through a simulation study. Finally, an application to a real data set is given.
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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 | 0.002 |
| 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