Notice bibliographique
Résumé
Many large scale surveys are designed to achieve acceptable reliability for large domains. Direct estimators for more detailed levels of aggregation are often judged to be unreliable due to small sample sizes. Estimation for small domains, often defined by geographic and demographic characteristics, is known as small area estimation. A common approach to small area estimation is to derive predictors under a specified mixed model for the direct estimators. A procedure of this type is developed for small areas defined by the cells of a two-way table.\nConstruction of small domain estimators using the Canadian Labour Force Survey (LFS) motivates the proposed model and estimation procedures. The LFS is designed to produce estimates of employment characteristics for certain pre-specified geographic and demographic domains. Direct estimators for specific occupations in small provinces are not published due to large estimated coefficients of variation. A preliminary study conducted in cooperation with Statistics Canada investigated estimation procedures for small areas defined by the cross-classification of occupations and provinces using data from a previous Census as auxiliary information. For consistency with published estimates, predictors are desired that preserve the direct estimators of the margins of the two-way table.\nOne method in the Statistics Canada study is based on a nonlinear mixed model for the direct estimators of the proportions. An initial predictor is defined to be a convex combination of the direct estimator and an estimator obtained by raking the Census totals to the direct estimators of the marginal totals. The estimators resulting from the raking operation are called the SPREE estimators and are expected to have smaller variances than the direct estimators. The weight assigned to the direct estimator depends on the relative magnitudes of an estimator of a random model component and an estimator of the sampling variance. The final predictors are defined by raking the initial predictors to the direct estimators of the marginal totals. Estimation of the mean squared error (MSE) of the predictors was not fully developed.\nThis dissertation addresses several issues raised by the procedure discussed above. First, the method above uses SPREE to estimate a fixed expected value. SPREE is unbiased if the Census interactions persist unchanged through time and is efficient if the direct estimators of the cell totals are realizations of independent Poisson random variables. A generalization of SPREE that is more efficient under a specified covariance structure is explored. A simulation study shows that predictors constructed under the specified covariance structure can have smaller MSE's than predictors calculated with the direct estimators of the variances. An estimator of the MSE of the initial convex combination of the direct estimator and the estimator of the fixed expected value is derived using Taylor linearizations. The LFS procedure uses a final raking operation to benchmark the predictors. A bootstrap procedure is investigated as a way to account for the effects of raking on the MSE's of the predictors. The procedures are applied to the Canadian Labour Force Survey, but the issues discussed are of general interest because they arise in many small area applications.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,044 | 0,019 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».