Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties
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Bibliographic record
Abstract
We examine several modified versions of the heteroskedasticity-consistent covariance matrix estimator of Hinkley and White. On the basis of sampling experiments which compare the performance of quasi t statistics, we find that one estimator, based on the jackknife, performs better in small samples than the rest. We also examine finite-sample properties using modified critical values based on Edgeworth approximations, as proposed by Rothenberg. In addition, we compare the power of several tests for heteroskedasticity and find that it may be wise to employ the jackknife heteroskedasticity-consistent covariance matrix even in the absence of detected heteroskedasticity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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