Biases And Variances Of Survey Estimators Based On Nearest Neighbor Imputation
Why this work is in the frame
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Bibliographic record
Abstract
Nearest neighbor imputation is one of the hot deck methods used to compensate for nonresponse in sample surveys. Although it has a long history of application, theoretical properties of the nearest neighbor imputation method are unknown prior to the current paper. We show that under some conditions, the nearest neighbor imputation method provides asymptotically unbiased and consistent estimators of functions of population means (or totals) , population distributions, and population quantiles. We also derive the asymptotic variances for estimators based on nearest neighbor imputation and consistent estimators of these asymptotic variances. Some simulation results show that the estimators based on nearest neighbor imputation and the proposed variance estimators have good performances. Key Words: Hot deck; Quantiles; Sample means; Variance estimation. 1 The research was supported by National Sciences and Engineering Research Council of Canada. 2 The research was supported by National S...
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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 | 0.004 |
| 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