Correlation structure selection for longitudinal data with diverging cluster size
Bibliographic record
Abstract
Abstract Correlation structure selection for non‐normal longitudinal data is very challenging for diverging cluster size because of the high‐dimensional correlation parameters involved and the complexity of the likelihood function for non‐normal longitudinal data. However identifying the correct correlation structure is important because it can improve estimation efficiency and testing power for longitudinal data. We propose to approximate the inverse of the empirical correlation matrix using a linear combination of candidate basis matrices, and select the correlation structure by identifying non‐zero coefficients of the basis matrices. This is carried out by minimizing penalized estimating functions, which balance the complexity and informativeness of modelling for the correlation matrix. The new approach does not require estimating each entry of the correlation matrix (except for an initial empirical estimate from the residuals), nor specifying the likelihood function, and can effectively handle non‐normal longitudinal data. The derivation of asymptotic theory for model selection consistency and oracle properties is challenging in the framework where the cluster size and the number of basis matrices are both diverging. Our numerical studies show that the proposed method performs satisfactorily for both normal and binary responses in this diverging framework.The Canadian Journal of Statistics44: 343–360; 2016 © 2016 Statistical Society of Canada
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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 | 0.030 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".