Unified scheme for testing for outliers in linear models
Bibliographic record
Abstract
Mickey et al. [Mickey, M.R., Dunn, O.J. and Clark, V., 1967, Note on use of stepwise regression in detecting outliers. Computers and Biomedical Research, 1, 105–111.] proposed a test for discordancy in linear models, which compares the sum of squares of residuals based on the complete model to that of a model obtained by deleting the potential outliers. John [John, J.A., 1978, Outliers in factorial experiments. Applied Statistics, 27, 111–119.] proposed a test procedure which treats the outliers as missing values and involves obtaining estimates of those missing values. In this article, we show that the two procedures are in fact equivalent. We also compare the performance of two methods of implementing these procedures. Owing to the wide variety of possible designs, tables of critical values are not readily available for testing for outliers in linear models. Therefore, we provide a program that will allow the user to input a design matrix, along with some data, and will output a p-value for testing for a specified number of outliers.
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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 | 0.037 | 0.094 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".