The Rao-Wu Rescaling Bootstrap: From theory to practice
Bibliographic record
Abstract
At Statistics Canada, variance estimation for complex surveys is mainly carried out using replication methods. The two replication methods which have been used in the last decade are the delete-one Primary Sampling Unit (PSU) jackknife and, more recently, the bootstrap. As Valliant (2007) rightly points out there are several variants of the bootstrap introduced by Efron (1979) in the i.i.d. case which are being used in survey sampling and we have to be clear which one is being referred to; at Statistics Canada we use solely the Rao-Wu rescaling bootstrap for production. Even though it was introduced in 1988 (see Rao and Wu (1988)), first implementations occurred only in the late 1990s. The Rao-Wu bootstrap is performed on both with and without replacement designs for which it yields sensible variance estimates for a variety of estimators including percentiles, a claim not matched by the delete-one PSU jackknife. Because of this, surveys which initially relied on the jackknife often make the switch to the bootstrap when the occasion arises, like during a survey re-design. The reader can get a good overview of what has been tried in survey sampling with regard to the bootstrap from Rust and Rao (1996), Shao (2003) and Lahiri (2003). The Rao-Wu variant of the original bootstrap procedure is appealing to survey users because it is simple to implement, it yields adequate variance estimates when the sample sizes are small (which is common with stratified designs) and it comes in the form of bootstrap weights. For any given methodology, making the transition from theory to practice is a challenge, and the Rao-Wu bootstrap
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".