Estimation and Variable Selection under the Function-on-scalar Linear Model with Covariate Measurement Error
Why this work is in the frame
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Bibliographic record
Abstract
Function-on-scalar linear regression has been widely used to model the relationship between a functional response and multiple scalar covariates.Its utility is, however, challenged by the presence of measurement error, a ubiquitous feature in applications.Naively applying usual function-on-scalar linear regression to error-contaminated data often yields biased inference results.Further, estimation of the model parameters is complicated by the presence of inactive variables, especially when handling data with a large dimension.Building parsimonious and interpretable function-on-scalar linear regression models is in urgent demand to handle error-contaminated functional data.In this paper, we study this important problem and investigate the measurement error effects.We propose a debiased loss function, combined with a sparsity-inducing penalty function, to
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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 | 0.001 | 0.001 |
| 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