Regression with right-censored high-dimensional data: An application with different imputation techniques
Bibliographic record
Abstract
This study aims to introduce four modified linear estimators for the right-censored high-dimensional data. Obviously, data of interest involves two important problems to be solved that are censorship and high dimensionality. This paper can be distinguished from other studies in the literature with that it achieves to handle these two problems simultaneously. The main contribution of the paper is merging weightedridge method with the imputation techniques to obtain more efficient estimators than its alternatives. To solve the censorship problem, four imputation techniques are considered based on machine learning algorithms kNN, sliding-windows, regression and support vector machines. The high-dimensionality problem is handled by the weighted ridge approach which provides estimator with less risk than its alternatives because it detects the covariates with a weak contribution via the post-selection procedure. To show the empirical performance of the introduced estimators, a simulation study is made and comparative results are presented. Results show that kNN and regression imputation basis WR esitmators show satisfying performances on estimation of the high-dimensional right-censored model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".