score-covariance-estimate
Score and compare covariance estimates with precise's assessor panel. Use when you need to judge an estimate out-of-sample or rank competing estimators — and especially in high dimensions, where the plain held-out likelihood is misleading.
适合你,如果你需要评估或比较不同协方差估计方法的准确性。
npx oh-my-skill add microprediction/precise/score-covariance-estimatecurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- microprediction/precise/score-covariance-estimatenpx oh-my-skill verify microprediction/precise/score-covariance-estimate怎么用
商店整理自技能原文 · 版本 d89c883 · 表述以原文为准使用precise库的assessor面板为协方差估计打分并排序，分数越高越好。需要提供测试数据或真实协方差（仅模拟）。
当需要判断协方差估计的样本外表现，或排序多个竞争估计时触发。尤其适合高维数据（维度接近样本量）。
技能原文 SKILL.md
Score / rank covariance estimates
from precise import all_assessors, assessor_from_name
for A in all_assessors():
s = A().score(cov, X_test=X_test, true_cov=Sigma_true) # higher = better
covis the estimate to judge;X_testis held-out data (rows = observations);true_covis the population covariance (only available in simulation).- Each assessor exposes
needs_dataandneeds_truth; pass what it needs. Truth-free assessors work on real data, truth-requiring ones (e.g.FrobeniusToTruth) only in simulation. - All assessors follow higher = better, so you can rank or argmax directly.
The one rule that matters in high dimensions
Do not rank estimators by the held-out Gaussian log-likelihood when p is comparable to n. The likelihood is dominated by the smallest, unidentifiable eigenvalues of the estimate; empirically it ranks estimators below chance in that regime. Instead use inversion-free or block judges:
| Regime / goal | Use | |---|---| | low dimension, well-conditioned | LogLikelihood (it is optimal here) | | high dimension (p/n near 1 or larger) | BlockPseudoLikelihood, SchurLikelihood, VariogramScore, FrobeniusToTruth (sim only) | | economic / portfolio relevance | GMVVariance (out-of-sample minimum-variance variance) | | forecasting a variance from a noisy proxy | a QLIKE / Bregman-consistent loss, not RMSE on the proxy — RMSE on a noisy variance proxy can rank inconsistently |
SchurLikelihood(gamma=...) is a tunable bridge: gamma=1 is the full likelihood (fragile in high-d), gamma=0 the robust block-diagonal one, interior values better-conditioned than either.
Don't over-read the numbers
- Rankings are ensemble-sensitive: a result on one data-generating process need not transfer. If the conclusion matters, sweep several generators and report per-regime (see the assess-covariance-method skill).
- A lower point error (RMSE) is not a tradable or actionable signal by itself.
- If you attach significance to a ranking, the loss differentials are usually dependent (overlapping windows, correlated assets); naive standard errors overstate significance — see the inference section of the assess-covariance-method skill.