A new face on two-phase sampling with calibration estimators
Bibliographic record
Abstract
This paper provides a framework for estimation by calibration in two-phase sampling designs. This work grew out of the continuing development of generalized estimation software at Statistics Canada. An important objective in this development is to provide a wide range of options for effective use of auxiliary information in different sampling designs. This objective is reflected in the general methodology for two-phase designs presented in this paper. We consider the traditional two-phase sampling design. A phase-one sample is drawn from the finite population and then a phase-two sample is drawn as a sub-sample of the first. The study variable, whose unknown population total is to be estimated, is observed only for the units in the phase-two sample. Arbitrary sampling designs are allowed in each phase of sampling. Different types of auxiliary information are identified for the computation of the calibration weights at each phase. The auxiliary variables and the study variables can be continuous or categorical. The paper contributes to four important areas in the general context of calibration for two-phase designs: (1) Three broad types of auxiliary information for two-phase designs are identified and used in the estimation. The information is incorporated into the weights in two steps: a phase-one calibration and a phase-two calibration. We discuss the composition of the appropriate auxiliary vectors for each step, and use a linearization method to arrive at the residuals that determine the asymptotic variance of the calibration estimator. (2) We examine the effect of alternative choices of starting weights for the calibration. The two “natural” choices for the starting weights generally produce slightly different estimators. However, under certain conditions, these two estimators have the same asymptotic variance. (3) We re-examine variance estimation for the two-phase calibration estimator. A new procedure is proposed that can improve significantly on the usual technique of conditioning on the phase-one sample. A simulation in section 10 serves to validate the advantage of this new method. (4) We compare the calibration approach with the traditional model-assisted regression technique which uses a linear regression fit at two levels. We show that the model-assisted estimator has properties similar to a two-phase calibration estimator.
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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 | 0.091 | 0.222 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".