Reproducing kernel‐based functional linear expectile regression
Bibliographic record
Abstract
Expectile regression is a useful alternative to conditional mean and quantile regression for characterizing a conditional response distribution, especially when the distribution is asymmetric or when its tails are of interest. In this article, we propose a class of scalar‐on‐function linear expectile regression models where the functional slope parameter is assumed to reside in a reproducing kernel Hilbert space (RKHS). Our approach addresses numerous drawbacks to existing estimators based on functional principal components analysis (FPCA), which make implicit assumptions about RKHS eigenstructure. We show that our proposed estimator can achieve an optimal rate of convergence by establishing asymptotic minimax lower and upper bounds on the prediction error. Under this framework, we propose a flexible implementation based on the alternating direction method of multipliers algorithm. Simulation studies and an analysis of real‐world neuroimaging data validate our methodology and theoretical findings and, furthermore, suggest its superiority over FPCA‐based approaches in numerous settings.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".