Dependent Bootstrap Confidence Intervals for a Population Mean
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Bibliographic record
Abstract
This study compares and analyzes the coverage probabilities and the averageinterval lengths of confidence interval for a population mean based on the dependentbootstrap procedure against those based on the independent bootstrap procedure. Bothdependent and independent bootstrap confidence intervals for a population mean arecomputed by the Bootstrap-t, Percentile, and Modified Percentile methods. Simulationsshow that the coverage probabilities of the dependent bootstrap confidence intervals aresimilar to those of the independent bootstrap confidence intervals. The average intervallengths of the dependent bootstrap method are shorter for most situations. For both theindependent and dependent bootstrap confidence intervals, the coverage probabilitiesincrease and the average interval lengths decrease as the sample size n increase for Normal, Gamma, and Chi-square distributions, as well as three methods used in this work.
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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 | 0.001 | 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 | 0.000 | 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