Bibliographic record
Abstract
Abstract Functional analysis of variance (ANOVA) models are often used to compare groups of functional data. Similar to the traditional ANOVA model, a common follow‐up procedure to the rejection of the functional ANOVA null hypothesis is to perform functional linear contrast tests to identify which groups have different mean functions. Most existing functional contrast tests assume independent functional observations within each group. In this article, we introduce a new functional linear contrast test procedure that accounts for possible time dependency among functional group members. The test statistic and its normalized version, based on the Karhunen–Loève decomposition of the covariance function and a weak convergence result of the error processes, follow respectively a mixture chi‐squared and a chi‐squared distribution. An extensive simulation study is conducted to compare the empirical performance of the existing and new contrast tests. We also present two applications of these contrast tests to a weather study and a battery‐life study. We provide software implementation and example data in the Supplementary Material.
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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 | 0.024 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".