A comparison of preliminary test, Stein-type and penalty estimators in gamma regression model
Why this work is in the frame
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Bibliographic record
Abstract
Owing to the broad applicability of gamma regression, we propose some improved estimators based on the preliminary test and Stein-type strategies to estimate the unknown parameters in a gamma regression model. These estimators are considered when it is suspected that the parameters may be restricted to a subspace of the parameter space. Two penalty estimators such as LASSO and ridge regression are also presented. An asymptotic theory for the preliminary test and Stein-type estimators is developed, and asymptotic distributional bias and asymptotic quadratic risk of the proposed estimators are obtained. Comprehensive Monte-Carlo simulation experiments are conducted. Comparisons are then made based on simulated relative efficiency to clarify the performance of the proposed estimators. Practitioners are recommended to use the positive-part Stein-type estimator since its performance is robust irrespective of the reliability of the subspace information. A real data on prostate cancer is considered to illustrate the performance of the proposed estimators.
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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