Adjusted Adaptive LASSO in High-dimensional Poisson Regression Model
Why this work is in the frame
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Bibliographic record
Abstract
The LASSO has been widely studied and used in many applications, but it not shown oracle properties. Depending on a consistent initial parameters vector, an adaptive LASSO showed oracle properties, which it is consistent in variable selection and asymptotically normal in coefficient estimation. In Poisson regression model, the usual adaptive LASSO using maximum likelihood coefficient estimators can result in very poor performance when there is multicollinearity. In this study, we proposed an adjusting of the adaptive LASSO to take into account the maximum likelihood standard errors of the coefficient parameters. The performance of the adaptive LASSO was demonstrated through simulation and real data. Our simulation and real data results show that adaptive LASSO has advantage in terms of both prediction and variable selection comparing with other existing adaptive penalized methods when the explanatory variables are highly correlated. Hence we can conclude that adaptive LASSO is a reliable adaptive penalized method in a Poisson regression model.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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