Sufficient dimension reduction in regression with missing predictors
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Bibliographic record
Abstract
Existing sufficient dimension reduction methods often resort to the complete- case analysis when the predictors are subject to missingness. The complete-case analysis is inefficient even with the missing completely at random mechanism be- cause all incomplete cases are discarded. In this paper, we introduce a nonparamet- ric imputation procedure for semiparametric regressions with missing predictors. We establish the consistency of the nonparametric imputation under the missing at random mechanism that allows the missingness to depend exclusively upon the completely observed response. When the missingness depends on both the com- pletely observed predictors and the response, we propose a parametric method to impute the missing predictors. We demonstrate the estimation consistency of the parametric imputation method through several synthetic examples. Our proposals are illustrated through comprehensive simulations and a data application.
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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 | 0.000 | 0.002 |
| 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