Asymmetric Estimation for Varying-Coefficient Additive Model with Functional Response in Reproducing Kernel Hilbert Space
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
Function-on-scalar regression models are extensively utilized in applications involving longitudinal or functional responses.Prior literature has established the minimax optimal bounds for both mean and quantile regression.This paper explores expectile regression as a natural extension to mean regression, particularly for modeling potential heteroscedasticity in data.We propose an expectile function-on-scalar regression model that focuses on asymmetrical regression of functional responses based on scalar predictors.Employing the structure of Reproducing Kernel Hilbert Space (RKHS), we have developed a statistically efficient expectile estimator.This estimator comes with theoretical backing, derived from the minimax rates of convergence in both random and fixed design contexts.Our extensive simulations demonstrate the robust performance of the proposed methods across various settings.Additionally, we present an empirical analysis using quality of life data from a breast cancer clinical trial, showcasing the practical utility of our method.
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 | 0.002 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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