Functional Linear Operator Quantile Regression for Sparse Longitudinal Data
Bibliographic record
Abstract
We propose a functional linear operator quantile regression (FLOQR) framework, which includes many important and useful functional data models, and devote to the new framework model for longitudinal data with the typically sparse and irregular designs.The non-smooth quantile loss and functional linear operator pose new challenges to functional data analysis for longitudinal data in both computation and theoretical development.To address the challenge, we propose the iterative surrogate least squares estimation approach for the FLOQR model, which transforms the response trajectories and establishes a new connection between FLOQR and functional linear operator model.In addition, we use Karhunen-Loève expansion to alleviate the problem of the nonexistence of the inverse of the covariance in the infinite-dimensional Hilbert space.Then, the approach is used to classic functional varying coefficient QR, functional linear QR, and functional varying coefficient QR with history index function for sparse longitudinal data by using functional principal components analysis through conditional expectation.The resulting technique is flexible and allows the prediction of an unobserved quantile response trajectory from sparse measurements of a predictor trajectory.Theoretically, we Statistica Sinica: Newly accepted Paper show that, after a constant number of iterations, the proposed estimator is asymptotic consistent for sparse designs.Moreover, asymptotic pointwise confidence bands are obtained for predicted quantile individual trajectories based on their asymptotic distributions.The proposed algorithms perform well in simulations, and are illustrated with longitudinal primary biliary liver cirrhosis data.
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.
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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".