Add SchurConditionalCovariance: structured Schur damping with a ridge-regressed hedge - #52
Merged
Merged
Conversation
…-regressed hedge The structured (Vecchia/conditional) counterpart of the entry-shrinkage SchurLedoitWolfCovariance. Forms each block's conditional regression b_k and Schur complement S_k, damps both by the OAS-style cross-block coupling reliability gamma*, and reassembles the implied PD covariance. The hedge uses a ridge-regularized inverse of the conditioning block, so the structured operation stays stable at n<p instead of blowing up through a raw R_cc^-1 -- the same regularization the allocation side applies to a hedge. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
The structured (Vecchia/conditional) counterpart of the entry-shrinkage
SchurLedoitWolfCovariance.For each block it forms the conditional regression (hedge)
b_kand Schur complementS_k, damps both by the OAS-style cross-block coupling reliability γ*, and reassembles the implied positive-definite covariance. The hedge uses a ridge-regularized inverse of the conditioning block, so the structured operation stays stable at n<p instead of blowing up through a rawR_cc⁻¹— the same regularization the allocation side applies to a hedge.n_blocks=1→ full EWA covariance.n_blocks=1== EwaCovariance, JSON state roundtrip.🤖 Generated with Claude Code