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A unit-diagonal covariance can have conditional covariance below the reciprocal global eigenvalue bound
Statement refuted
For every real symmetric positive-definite covariance matrix with unit diagonal, every coordinate set for which both and are nonempty and is invertible, and constants , , , and satisfying the Schur conditional covariance satisfies . Here .
Statement references
Facts & Assumptions
Given: The matrix inequality stated above is proposed for all positive-definite unit-diagonal and admissible .
For a block matrix with invertible leading block, the expression is the Schur complement; here it is defined by the displayed formula.
Counterexample
Fix any integer , set , , and take with , , , and . The vectors and have eigenvalues and for every , and these subspaces span ; hence is positive definite with unit diagonal and maximum eigenvalue . Also , so its minimum eigenvalue is and its inverse infinity norm is , while . Thus all stated covariance hypotheses hold.
Substitution in [A1] gives , so since . Direct block multiplication gives , whose lower-right block is , the inverse of the conditional covariance rather than the conditional covariance itself. Thus the proposed bound is false.
Source notes
For this block family, the lower-right block of is , the inverse of , not itself.
Used by
Nothing in the library uses this result yet.
Dependency tree · 0 levels
Nothing. This result depends on no other item in the library.
Sources
- Wainwright, Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using L1-Constrained Quadratic Programming (Lasso), 2009, §IV.A (standard reference, not scraped)
- Wainwright, Sharp Thresholds for High-Dimensional and Noisy Recovery of Sparsity, 2006, Appendix D.6 (standard reference, not scraped)