POWER COMPARISON OF SOME TESTS FOR DETECTING A CHANGE IN THE MULTIVARIATE MEAN
Bibliographic record
Abstract
Using Monte Carlo methods, we compare the power of three tests based on each of N ≥ 2 p-dimensional random vectors x 1,…,x N to decide if the means μi of the x i's are all equal against the alternative that a change has occurred at some point r (i.e., μ1 = μ2 = ··· = μ r ≠ μ r+1 = μ N ). The vectors x i are assumed to have multivariate normal distributions with common unknown covariance matrix Σ. Two of these tests, a likelihood ratio test and a generalization of Bayes test have been proposed by Srivastava and the third test is a generalization of a test proposed by Sen and Srivastava. It is found that for detecting moderate to large shifts, the test based on the LR statistics performs best when the change occurs near the beginning or the end, while the generalization of Sen and Srivastava's test performs best when the change occurs near the middle. A third test, a multivariate generalization of a univariate Bayes test is slightly inferior. However, for detecting small shifts, or for large sample sizes (N≥60) and moderate p, all three tests perform similarly in the cases we considered. The sequential stopping rule along with pinching-algorithm of Dunn are used to provide tables of simulated percentiles.
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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 | 0.114 | 0.437 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".