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Sample-covariance resolvent deterministic equivalents
Statement
Assume the Axiom of Choice. Let be one infinite iid array of complex random variables with , , and . For each positive integer , let be positive with and let be its upper-left block. Let be deterministic Hermitian positive semidefinite with uniformly bounded operator norm and empirical eigenvalue measures . Let be deterministic unit vectors such that, for every , Define and for Borel . Then almost surely converges weakly to the probability measure whose Stieltjes transform is the unique satisfying , with , and
Statement references
Bai, Miao, and Pan, Theorem 1 and complete Section 2 proof, printed pp. 8–16 (full paper PDF).
Silverstein, Theorem 1.1 and complete Section 3 proof (full paper PDF).
Facts & Assumptions
Given: The common nested iid array, finite-fourth-moment law, deterministic population matrices and directions, aspect ratio, and AC in the Statement.
The Axiom of Choice permits a choice function for every family of nonempty sets (The Axiom of Choice).
For bounded complex iid entries with the stated population and direction hypotheses, the directional measures converge almost surely to (Directional deterministic equivalent for bounded sample-covariance entries).
For a centered complex iid finite-fourth-moment array with , the same nested-block and aspect-ratio conventions give almost surely, where and ; the bound includes (A coarse complex sample-covariance norm bound from real edge bounds).
A Hermitian directional spectral measure is a probability law (A Hermitian directional spectral measure is a probability law).
Tight probability laws on a Polish space have weakly convergent subsequences under AC (Prokhorov tightness theorem on polish spaces).
Equal Stieltjes transforms determine probability measures on the real line (Stieltjes inversion and uniqueness for probability measures on the real line).
A Polish space is a separable topological space whose topology is induced by a complete metric (Polish spaces are separable completely metrizable spaces).
Iid entries are mutually independent and share one common law (Identical distribution and IID families).
The directional measure is for a deterministic unit vector (Directional spectral measure of a Hermitian matrix).
The Stieltjes transform convention is on (Stieltjes transform of a probability measure on the real line).
Weak convergence means convergence of integrals against all bounded continuous real-valued functions (Weak convergence of borel probability measures).
The operator norm is the maximum of over unit vectors in the domain (The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||).
Proof
Let have the common entry law. For integer , set , , and . Then , and because and . For all sufficiently large , ; put . These entries are centered, bounded, iid and have unit second absolute moment, while is centered iid with finite fourth moment. At matrix level, , where and are the common upper-left blocks formed from and .
For each fixed sufficiently large integer , the original, standardized, and residual arrays have second absolute moments , , and , respectively. Applying the variance-scaled bound [F2] to each gives almost surely where . If , the centered residual entries are zero almost surely; countability of the double array gives for every on one probability-one event, so the residual bound still holds. Intersect these events over the countable set of such . Writing , the identity yields where . For positive semidefinite and , the resolvent identity gives .
Fix and a deterministic unit direction sequence . For each such , [F1] applied to gives almost surely, with the transform convention [F9]. By step 2.1, on the probability-one event obtained by intersecting the events from step 2.1 with the almost-sure convergence event for this fixed truncation level. The right side tends to zero as , so the directional Stieltjes transforms converge almost surely at every fixed . Intersecting over a countable dense set gives simultaneous convergence there.
By [F2], is almost surely eventually bounded. Thus the directional laws are eventually supported on one compact interval; [F3] makes them probability measures and [F8] gives their exact spectral-measure meaning. They are therefore tight. The half-line is Polish: it is closed in the complete real line and has dense subset , as required by [F6]. By [A1] and [F4], each subsequence has a weakly convergent further subsequence. Its transform equals on the dense set by step 3.1; continuity of Stieltjes transforms extends equality to , and [F5] identifies the subsequential limit as . The bounded-continuous-test definition [F10] yields the stated weak convergence. Every subsequence has the same further limit, proving the asserted almost-sure limit.
Source notes
Bai, Miao, and Pan, Theorem 1 and Section 2, printed pp. 8–16 (full paper PDF). The sampling convention is the upper-left block of one iid double array. The truncation comparison uses the separately proved real-edge norm supplier; it does not assume weak convergence alone controls spectral edges.
Depends on
- The Axiom of Choice
- Identical distribution and IID families
- Polish spaces are separable completely metrizable spaces
- The operator norm is zero on the zero domain and otherwise is max_{||v||=1} ||Tv||
- Directional spectral measure of a Hermitian matrix
- Stieltjes transform of a probability measure on the real line
- Weak convergence of borel probability measures
- A coarse complex sample-covariance norm bound from real edge bounds
- A Hermitian directional spectral measure is a probability law
- Stieltjes inversion and uniqueness for probability measures on the real line
- Directional deterministic equivalent for bounded sample-covariance entries
- Prokhorov tightness theorem on polish spaces
Used by
- Fixed negative-axis directional resolvent integrals converge Corollary
- Isotropic minimum-norm risk limits under a positive spectral gap Corollary
- An anisotropic population with a balanced deterministic direction Example
- An isotropic finite-fourth-moment design Example
- At aspect one the reciprocal-spectrum limit diverges Example
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Sources
- Z. D. Bai, B. Q. Miao, and G. M. Pan, On Asymptotics of Eigenvectors of Large Sample Covariance Matrix (standard reference, not scraped)
- J. W. Silverstein, Strong Convergence of the Empirical Distribution of Eigenvalues of Large Dimensional Random Matrices (standard reference, not scraped)