Semiparametric estimation for measurement error models with validation data
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 Statistics45: 185–201; 2017 © 2017 Statistical Society of Canada
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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 | 0.049 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".