Measurement error and convolution in generalized functions spaces
Bibliographic record
Abstract
This paper considers convolution equations that arise from problems such as deconvolution and non-parametric regression with errors in variables. The equations are examined in spaces of generalized functions to account for possible singularities; identi cation is proved for a wide class of problems. Conditions for well-posedness in the topology of generalized functions are derived for the deconvolution problem and some regressions; an example shows that even in this weak topology well-posedness may not hold. Well-posedness is crucial for consistency of non-parametric deconvolution and important in cases when a non-parametric model is mis-speci ed as parametric. Stochastic properties and convergence for generalized random processes are derived for solutions of convolution equations. This paper focuses on independent data. Deconvolution estimation for a generalized function on a bounded support in a xed design case is examined; the rate for a shrinkage deconvolution estimator useful for a sparse support is derived. A consistent non-parametric
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".