Bayesian growth curve model useful for high-dimensional longitudinal data
Bibliographic record
Abstract
Traditional inference on the growth curve model (GCM) requires ‘small p large n’ (n≫p) and cannot be applied in high-dimensional scenarios, where we often encounter singularity. Several methods are proposed to tackle the singularity problem, however there are still limitations and gaps. We consider a Bayesian framework to derive a statistic for testing a linear hypothesis on the GCM. Extensive simulations are performed to investigate performance and establish optimality characteristics. We show that the test overcomes the challenge of high-dimensionality and possesses all the desirable optimality characteristics of a good test - it is unbiased, symmetric and monotone with respect to sample size and departure from the null hypotheses. The results also indicate that the test performs very well, possessing a level close to the nominal value and high power in rejecting small departures from the null. The results also show that the test overcomes limitations of a previously proposed test. We illustrated practical applications using a publicly available time course genetic data on breast cancer, where we used our test statistic for gene filtering. The genes were ranked according to the value of the test statistic and the top five genes were annotated.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".