Estimation effects on powers of two simple test statistics in identifying an outlier in linear models
Bibliographic record
Abstract
In this paper, we consider two well known, namely the maximum studentized residual (MSR) and maximum normed residual (MNR), test statistics for the detection of a single outlier in linear models. The regression and variance parameters involved in the test statistics are estimated by the traditional least square method (LSM) as well as by two different robust methods (RMs). It is shown through a simulation study that even though RM when compared with LSM produces better estimates for the parameters of the model in the presence of an outlier, the robust estimates-based MSR and MNR tests are however found to be equally or less powerful than the LS estimates-based MSR and MNR tests. This suggests that one should use the LS estimates-based test for the detection of an outlier. Next, if the LS estimates-based test confirms the presence of an outlier, one should however estimate the parameters by using RM.
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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 | 0.182 | 0.682 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".