Bibliographic record
Abstract
To bootstrap a regression problem, pairs of response and explanatory variables or residuals can be resampled, according to whether we believe that the explanatory variables are random or fixed.In the latter case, different residuals have been proposed in the literature, including the ordinary residuals (Efron, 1979), standardized residuals (Bickel andFreedman, 1983) and studentized residuals (Weber, 1984).Freedman (1981) has shown that the bootstrap from ordinary residuals is asymptotically valid when the number of cases increases and the number of variables is fixed while Weber (1984) has done the same for studentized residuals.Bickel and Freedman (1983) have shown the asymptotic validity for ordinary residuals when the number of variables as well as the number of cases increase provided that the ratio of the two converges to 0 at an appropriate rate.In this paper, we introduce the use of Best Linear Unbiased Scaled (BLUS) residuals in bootstrapping regression models.The main advantage of the BLUS residuals, introduced in Theil (1965), is that they are uncorrelated.The main disadvantage is that only n -p residuals can be computed for a regression problem with n cases and p variables.The asymptotic results of Freedman (1981) and Bickel and Freedman (1983) for the ordinary (and standardized) residuals are generalized to the BLUS residuals.A small simulation study shows that even though only n -p residuals are available, bootstrapping BLUS residuals is as good in small samples, and sometimes better, than bootstrapping from standardized or studentized residuals.
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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 | 0.023 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".