Semiparametric estimation for measurement error models with validation data
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
Abstract We consider regression problems where error‐prone surrogates of true predictors are collected in a primary data set while accurate measurements of the predictors are available only in a relatively small validation data set. We propose a new class of semiparametric estimators for the regression coefficients based on expected estimating equations, where the relationship between the surrogates and the true predictors is modelled nonparametrically using a kernel smoother trained with the validation data. The new methods are developed under two different scenarios where the response variable is either observed or not observed in the validation data set. The proposed estimators have a natural connection with the fractional imputation method. They are consistent, asymptotically unbiased, and normal in both scenarios. Our simulation studies show that the proposed estimators are superior to competitors in terms of bias and mean squared error and are quite robust against the misspecification of the regression model and bandwidth selection. A real data application to the Korean Longitudinal Study of Aging is presented for illustration. The Canadian Journal of Statistics 45: 185–201; 2017 © 2017 Statistical Society of Canada
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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 | 0.002 | 0.017 |
| 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 | 0.001 | 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