Bibliographic record
Abstract
Abstract Dimension reduction for regression analysis has been one of the most popular topics in the past two decades. It sees much progress with the introduction of the inverse regression, centered around the two key methods, sliced inverse regression (SIR) and sliced average variance estimation (SAVE). It is well known that SIR works poorly when the inverse conditional expectation is close to being nonrandom. SAVE and its many generalizations, which do not suffer from this drawback, lag behind SIR in many other circumstances. Usually a certain weighted hybrid of SIR and SAVE is necessary to improve overall performance. However, it is difficult to find the optimal mixture weights in a hybrid, and most such hybrid methods, as well as SAVE, require the restrictive constant (conditional) variance condition. We propose a much weaker condition and a new accompanying algorithm. This enables us to create several new central matrices that perform very favourably to existing central matrix based methods without referring to hybrids. The Canadian Journal of Statistics 41: 421–438; 2013 © 2013 Statistical Society of Canada
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".