Errors-in-variables models: a generalized functions approach
Bibliographic record
Abstract
Identi…cation in errors-in-variables regression models was recently extended to wide models classes by S. Schennach (Econometrica, 2007) (S) via use of generalized functions. In this paper the problems of nonand semi- parametric identi…cation in such models are re-examined. Nonparametric identi…cation holds under weaker assumptions than in (S); the proof here does not rely on decomposition of generalized functions into ordinary and singular parts, which may not hold. Conditions for continuity of the identi…cation mapping are provided and a consistent nonparametric plug-in estimator for regression functions in the L1space constructed. Semiparametric identi…cation via a …nite set of moments is shown to hold for classes of functions that are explicitly The support of the Social Sciences and Humanities Research Council of Canada (SSHRC), the Fonds québecois de la recherche sur la société et la culture (FRQSC) is gratefully acknowledged. The author thanks participants in the Cowles Foundation conference, the UK ESG, CEA 2009 and Stats in the Chateau meetings and P.C.B.Phillips, X. Chen and Denis Nekipelov for illuminating discussions and suggestions. An anonymous referee provided a very thorough report containing many important points and suggestions. The remaining errors are all mine. 1 characterized; unlike (S) existence of a moment generating function for the measurement error is not required.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".