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The multivariate trimmed-mean rate follows from a correct-scale slab event
Statement
Let be a finite covariance matrix in , , with , mean , and as in Basic finite covariance matrix identities for robust mean bounds. Let , , , and . Define
Fix and any integer with and any observed input tuple to Adaptive multivariate trimmed mean. If is nonempty and every satisfies , then the algorithm's output satisfies
where
In particular, recovers the original constant and 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.
The selector returns a point of when has least element ; if , it returns a point in the all-scale intersection; the sets are nested (Adaptive multivariate trimmed mean, Totality and measurability of the repaired multivariate trimmed-mean selector).
The covariance matrix is positive semidefinite and (Basic finite covariance matrix identities for robust mean bounds).
The logarithm is increasing and satisfies for (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
Given: The positive-trace covariance, parameters, dyadic index, arbitrary observed input tuple, and correct-scale slab condition in the Statement.
Since , . If , [F1] puts the algorithm output in . If is nonempty and proper, its least element satisfies , so nesting in [F1] gives and the output again belongs to . Thus the event condition implies .
The definition of gives . Since , square-root subadditivity and [F3] bound the second term by . By [F2] the final term is at most . These three coefficients are bounded by , giving the stated implication. At this is exactly the previous constant.
Depends on
- Adaptive multivariate trimmed mean
- Totality and measurability of the repaired multivariate trimmed-mean selector
- Basic finite covariance matrix identities for robust mean bounds
- The natural logarithm as the inverse of the exponential function
- Order, continuity, range, and the product, quotient, and reciprocal laws for the natural logarithm
Used by
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Sources
- Gábor Lugosi and Shahar Mendelson, Robust Multivariate Mean Estimation: The Optimality of Trimmed Mean, Theorem 2 (standard reference, not scraped)
- Gábor Lugosi, Mean Estimation in High Dimension, §3.2 (standard reference, not scraped)