Bibliographic record
Abstract
The shrinkage method of Ledoit and Wolf (2003; 2004a; 2004b) has shown certain success in estimating a well-conditioned covariance matrix for high dimensional portfolios. This paper generalizes the shrinkage method of Ledoit and Wolf to a multivariate shrinkage setting, by which the well-conditioned covariance matrix is estimated using the weighted averaging of multiple priors, instead of single ones. In fact, it can be argued that the generalized multivariate shrinkage approach reduces estimation errors and uncertainty when projecting the true covariance matrix onto the line, spanned by priors joining to the sample covariance matrix. Hence, the generalized multivariate shrinkage is less subjected to sampling variation. Empirically, I use the U.S. firms to form portfolios for out-of-sample forecast. Using Ledoit and Wolf's approach as benchmark, out-of-sample portfolios constructed from the proposed method gain significant variance reductions and sizable improvement of information ratios.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".