A group bridge approach for component selection in nonparametric accelerated failure time additive regression model
Why this work is in the frame
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Bibliographic record
Abstract
We study a nonparametric accelerated failure time (AFT) additive regression model whose covariates have nonparametric effects on the censored survival time. The proposed model is more flexible than the linear AFT model and can be used to perform dimension reduction and model building. Specifically, it can be used to discover the functional forms of all the covariates, whether a function is a zero or nonzero component; if it is a nonzero component, whether it is linear or nonlinear. First, we treat all the components as unknown nonlinear functions. B-splines are used to model these nonparametric components. A group bridge penalized variable selection approach based on the inverse probability-of-censoring weighted least squares is developed to select important nonparametric components and discover their functional forms simultaneously. Meanwhile, we compare the group bridge and group LASSO methods. The simulation results demonstrate that the group bridge method provides more accurate estimation and better selection performance than the group LASSO method, and the proposed method has satisfactory performance even with relatively high censoring rates. Two real data analyses are used to illustrate the application of the proposed method to censored survival data.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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