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LemmaStatement: AI-adaptedProof: AI-adaptedPipeline-generatedjudge pass (gpt-6-luna)
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The multivariate trimmed-mean rate follows from a correct-scale slab event

Statement

Let Σ be a finite covariance matrix in Rd, d≥1, with tr⁡Σ>0, mean μ, and 0≤λmax⁡(Σ)≤tr⁡Σ as in Basic finite covariance matrix identities for robust mean bounds. Let 0≤η≤1/10, 0<δ<1, N≥2560log⁡(2/δ), and ε=max⁡{10η,2560log⁡(2/δ)/N}. Define

Q0=max⁡{256εtr⁡ΣN, 16λmax⁡(Σ)ε}.

Fix b>0 and any integer i0 with 2i0∈[2Q0,4Q0) and any observed input tuple to Adaptive multivariate trimmed mean. If Ki0 is nonempty and every z∈Ki0 satisfies ∥z−μ∥2≤bεQ0, then the algorithm's output satisfies

∥μ^−μ∥2≤cb[tr⁡ΣN+λmax⁡(Σ)log⁡(1/δ)N+λmax⁡(Σ)η],

where

cb=bmax⁡{256+162560log⁡2, 162560, 1610}.

In particular, b=4 recovers the original constant and b=16 applies to the corrected slab proposition. This is a deterministic implication from the slab event; it does not assert that the event has high probability.

Facts & Assumptions

Given: The parameters, covariance matrix, mean, dyadic index, arbitrary observed data tuple, and the stated nonempty correct-scale slab condition.

[F1]

The selector returns a point of Kj when S={j:Kj≠∅} has least element j; if S=Z, it returns a point in the all-scale intersection; the sets Kj are nested (Adaptive multivariate trimmed mean, Totality and measurability of the repaired multivariate trimmed-mean selector).

[F2]

The covariance matrix is positive semidefinite and 0≤λmax⁡(Σ)≤tr⁡Σ (Basic finite covariance matrix identities for robust mean bounds).

[F3]

The logarithm is increasing and satisfies log⁡(2/δ)=log⁡2+log⁡(1/δ) for 0<δ<1 (The natural logarithm as the inverse of the exponential function, Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm).

Proof

technique · direct

Given: The positive-trace covariance, parameters, dyadic index, arbitrary observed input tuple, and correct-scale slab condition in the Statement.

1.1F1givenalgebra

Since Ki0≠∅, i0∈S. If S=Z, [F1] puts the algorithm output in ⋂i∈ZΓ(2i)⊆Ki0. If S is nonempty and proper, its least element j∗ satisfies j∗≤i0, so nesting in [F1] gives Kj∗⊆Ki0 and the output again belongs to Ki0. Thus the event condition implies ∥μ^−μ∥2≤bεQ0.

2.1F2F3step 1.1algebra∎

The definition of Q0 gives bεQ0≤256btr⁡Σ/N+16bλmax⁡(Σ)ε. Since ε≤10η+2560log⁡(2/δ)/N, square-root subadditivity and [F3] bound the second term by 16b10λmax⁡(Σ)η+16b2560λmax⁡(Σ)log⁡(1/δ)/N+16b2560λmax⁡(Σ)log⁡2/N. By [F2] the final term is at most 16b2560log⁡2tr⁡Σ/N. These three coefficients are bounded by cb, giving the stated implication. At b=4 this is exactly the previous constant.

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