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At aspect one the reciprocal-spectrum limit diverges
Example
Assume the Axiom of Choice. Let be one infinite iid complex array with mean zero, unit second absolute moment and finite fourth moment. Let be a positive integer with , set , and choose deterministic unit vectors . The directional measures converge almost surely to the aspect-one Marchenko–Pastur law with density on and no atom at zero. For every fixed , their negative-axis integrals converge to , and these limiting integrals tend to as ; the smallest positive sample-covariance eigenvalue tends to zero, so there is no positive lower-edge gap for reciprocal-spectrum closure.
Example references
Facts & Assumptions
Given: AC, the nested complex iid entry law, , aspect ratio one, and deterministic unit directions.
The Axiom of Choice (The Axiom of Choice).
The isotropic directional law converges to the Marchenko–Pastur law (Sample-covariance resolvent deterministic equivalents).
Fixed negative-axis integrals converge by bounded-continuous-test convergence (Fixed negative-axis directional resolvent integrals converge).
At aspect one the Marchenko–Pastur density is on and has no zero atom (Marchenko–Pastur measure with aspect ratio gamma, The Marchenko–Pastur formula defines a probability law with the fixed-point transform).
The finite complex directional measure is , with nonnegative weights and support on the nonnegative eigenvalues of (Directional spectral measure of a Hermitian matrix). Weak convergence means convergence of integrals against every bounded continuous real-valued function (Weak convergence of borel probability measures).
The isotropic limit equals the aspect-one Marchenko–Pastur law because both transforms satisfy the unique fixed-point equation (Uniqueness of the deformed Marchenko–Pastur fixed point, The Marchenko–Pastur formula defines a probability law with the fixed-point transform, Stieltjes inversion and uniqueness for probability measures on the real line).
Monotone convergence passes increasing nonnegative functions through their measure integrals (Monotone convergence for the integral).
The Dirac measure is the unit point mass at (The Dirac set function at a point).
The nonnegative integral is monotone and homogeneous under positive finite scalars; the integral of a measurable indicator is its set measure, by the simple-integral formula and agreement with the nonnegative integral (Monotonicity and nonnegative homogeneity of the nonnegative integral, The integral of a nonnegative simple function, The nonnegative integral agrees with the simple integral on simple functions).
Verification
Given: The hypotheses and notation in the Example.
With and , the population-resolvent condition holds for every unit vector, so [F1] gives convergence to . The transform of the law in [F3] satisfies the same fixed-point equation; [F5] identifies . Substituting , into the density in [F3] gives on and zero atom.
For fixed , is bounded and continuous, so [F2] gives the asserted limit of the negative-axis integrals. For , [F3] gives ; hence , whose integral at zero diverges. Along , the nonnegative functions increase to for . The law has no atom at zero by [F3], and [F6] gives . Since these integrals are nondecreasing as , the full limit is .
Work on the probability-one weak-convergence event of step 1.1. For each integer , set and , and define the bounded continuous real function on . It vanishes outside , is positive exactly on , and is at least on the middle half of that interval. On , [F3] gives . Applying the order and scalar rules in [F8] first to under , then to this density lower bound on under Lebesgue measure, gives , since has length . By [F4] and step 1.1, , so for each this sample integral is eventually positive. Since , [F8] gives . The finite atomic set formula in [F4] makes the right side ; therefore there is an actual eigenvalue in .
These conclusions hold for all on the same weak-convergence event, since its bounded-continuous-test property applies to each . In particular ensures at least one positive eigenvalue for every sufficiently large , so the smallest positive eigenvalue is eventually defined. For every fixed , step 2.2 gives for all sufficiently large ; since , it follows that on that probability-one event. This excludes an eventual uniform positive lower bound and proves the stated gap failure for the full complex entry class without claiming convergence of finite-sample reciprocal traces.
Source notes
Chapter 3 of Bai and Silverstein (full book PDF).
Theorem 1 of Bai, Miao, and Pan (full paper PDF).
Depends on
- The Axiom of Choice
- Marchenko–Pastur measure with aspect ratio gamma
- The Dirac set function at a point
- The Marchenko–Pastur formula defines a probability law with the fixed-point transform
- Uniqueness of the deformed Marchenko–Pastur fixed point
- Stieltjes inversion and uniqueness for probability measures on the real line
- Sample-covariance resolvent deterministic equivalents
- Directional spectral measure of a Hermitian matrix
- Weak convergence of borel probability measures
- Monotonicity and nonnegative homogeneity of the nonnegative integral
- The integral of a nonnegative simple function
- The nonnegative integral agrees with the simple integral on simple functions
- Fixed negative-axis directional resolvent integrals converge
- Monotone convergence for the integral
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Sources
- Z. D. Bai and J. W. Silverstein, Spectral Analysis of Large Dimensional Random Matrices, 2nd ed. (standard reference, not scraped)
- Z. D. Bai, B. Q. Miao, and G. M. Pan, On Asymptotics of Eigenvectors of Large Sample Covariance Matrix (standard reference, not scraped)