Imputation en présence de données contenant des zéros
Bibliographic record
Abstract
L’imputation simple est très souvent utilisée dans les enquêtes pour compenser\npour la non-réponse partielle. Dans certaines situations, la variable nécessitant\nl’imputation prend des valeurs nulles un très grand nombre de fois. Ceci est très\nfréquent dans les enquêtes entreprises qui collectent les variables économiques.\nDans ce mémoire, nous étudions les propriétés de deux méthodes d’imputation\nsouvent utilisées en pratique et nous montrons qu’elles produisent des estimateurs\nimputés biaisés en général. Motivé par un modèle de mélange, nous proposons\ntrois méthodes d’imputation et étudions leurs propriétés en termes de biais.\nPour ces méthodes d’imputation, nous considérons un estimateur jackknife de la\nvariance convergent vers la vraie variance, sous l’hypothèse que la fraction de\nsondage est négligeable. Finalement, nous effectuons une étude par simulation\npour étudier la performance des estimateurs ponctuels et de variance en termes\nde biais et d’erreur quadratique moyenne.
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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 | 0.022 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".