Bayesian growth curve model useful for high-dimensional longitudinal data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it