S Dallaporta

Eigenvalue variance bounds for Wigner and covariance random matrices

This work is concerned with finite range bounds on the variance of individual eigenvalues of Wigner random matrices, in the bulk and at the edge of the spectrum, as well as for some intermediate eigenvalues. Relying on the GUE example, which needs to be investigated first, the main bounds are extended to families of Hermitian Wigner matrices by means of the Tao and Vu Four Moment Theorem and recent localization results by Erdös, Yau and Yin. The case of real Wigner matrices is obtained from interlacing formulas. As an application, bounds on the expected 2-Wasserstein distance between the empirical spectral measure and the semicircle law are derived. Similar results are available for random covariance matrices.

Two different models of random Hermitian matrices were introduced by Wishart in the twenties and by Wigner in the fifties. Wishart was interested in modeling tables of random data in multivariate analysis and worked on random covariance matrices. In this paper, the results for covariance matrices are very close to those for Wigner matrices. Therefore, it deals mainly with Wigner matrices. Definitions and results regarding covariance matrices are available in the last section.

Random Wigner matrices were first introduced by Wigner to study eigenvalues of infinite-dimensional operators in statistical physics (see [START_REF] Mehta | Random matrices[END_REF]) and then propagated to various fields of mathematics involved in the study of spectra of random matrices. Under suitable symmetry assumptions, the asymptotic properties of the eigenvalues of a random matrix were soon conjectured to be universal, in the sense they do not depend on the individual distribution of the matrix entries. This opened the way to numerous developments on the asymptotics of various statistics of the eigenvalues of random matrices, such as for example the global behavior of the spectrum, the spacings between the eigenvalues in the bulk of the spectrum or the behavior of the extreme eigenvalues. Two main models have been considered, invariant matrices and Wigner matrices. In the invariant matrix models, the matrix law is unitary invariant and the eigenvalue joint distribution can be written explicitly in terms of a given potential. In the Wigner models, the matrix entries are independent (up to symmetry conditions). The case where the entries are Gaussian is the only model belonging to both types. In the latter case, the joint distribution of the eigenvalues is thus explicitly known and the previous statistics have been completely studied.

One main focus of random matrix theory in the past decades was to prove that these asymptotic behaviors were the same for non-Gaussian matrices (see for instance [START_REF] Anderson | An introduction to random matrices[END_REF], [START_REF] Bai | Spectral analysis of large dimensional random matrices[END_REF] and [START_REF] Pastur | Eigenvalue distribution of large random matrices[END_REF]).

However, in several fields such as computer science or statistics for example, asymptotic statements are often not enough, and more quantitative finite range results are required. Several recent developments have thus been concerned with non-asymptotic random matrix theory towards quantitative bounds (for instance on the probability for a certain event to occur) which are valid for all N, where N is the size of the given matrix. See for example [START_REF] Vershynin | Introduction to the non-asymptotic analysis of random matrices[END_REF] for an introduction to some problems considered in non-asymptotic random matrix theory. In this paper, we investigate in this respect variance bounds on the eigenvalues of families of Wigner random matrices.

Wigner matrices are Hermitian or real symmetric matrices M N such that, if M N is complex, for i < j, the real and imaginary parts of (M N ) ij are independent and identically distributed (iid) with mean 0 and variance 1 2 , (M N ) ii are iid, with mean 0 and variance 1. In the real case, (M n ) ij are iid, with mean 0 and variance 1 and (M N ) ii are iid, with mean 0 and variance 2. In both cases, set W N = 1 √ N M N . An important example of Wigner matrices is the case where the entries are Gaussian. If M N is complex, then it belongs to the so-called Gaussian Unitary Ensemble (GUE). If it is real, it belongs to the Gaussian Orthogonal Ensemble (GOE). The matrix W N has N real eigenvalues λ 1 • • • λ N . In the Gaussian case, the joint law of the eigenvalues is known, allowing for complete descriptions of their limiting behavior both in the global and local regimes (see for example [START_REF] Anderson | An introduction to random matrices[END_REF], [START_REF] Bai | Spectral analysis of large dimensional random matrices[END_REF] and [START_REF] Pastur | Eigenvalue distribution of large random matrices[END_REF]).

Among universality results, at the global level, the classical Wigner's Theorem states that the empirical distribution L N = 1 N N j=1 δ λ j on the eigenvalues of W N converges weakly almost surely to the semicircle law

dρ sc (x) = 1 2π √ 4 -x 2 ½ [-2
,2] (x)dx (see for example [START_REF] Bai | Spectral analysis of large dimensional random matrices[END_REF] for a proof in the more general setting). This gives the global asymptotic behavior of the spectrum. However, this theorem is not enough to deduce some information on individual eigenvalues. Define, for all 1 j N, the theoretical location of the j th eigenvalue γ j by γ j -2 dρ sc (x) = j N . Bai and Yin proved in [START_REF] Bai | Necessary and sufficient conditions for almost sure convergence of the largest eigenvalue of a Wigner matrix[END_REF] with assumptions on higher moments that almost surely the smallest and the largest eigenvalues converge to their theoretical locations, which means that λ N → 2 and λ 1 → -2 almost surely (see also [START_REF] Bai | Spectral analysis of large dimensional random matrices[END_REF] for a proof of this theorem). From this almost sure convergence of the extreme eigenvalues and Wigner's Theorem, it is possible to deduce information on individual eigenvalues in the bulk of the spectrum. Indeed, according to the Glivenko-Cantelli Theorem (see for example [START_REF] Dudley | Real analysis and probability[END_REF]), the normalized eigenvalue function 1 N N x , where N x is the number of eigenvalues which are in (-∞, x], converges uniformly on R almost surely to the distribution function of the semicircle law G (with no more assumptions on the matrix entries). Then, using that 1 N N λ j = j N = G(γ j ) together with crude bounds on the semicircle density function and the fact that λ j is almost surely between -2ε and 2 + ε shows that almost surely λ jγ j → 0 uniformly for ηN j (1η)N (for any fixed η > 0).

At the fluctuation level, eigenvalues inside the bulk and at the edge of the spectrum do not have the same behavior. Tracy and Widom showed in [START_REF] Tracy | Level-spacing distributions and the Airy kernel[END_REF] that the largest eigenvalue fluctuates around 2 according to the so-called Tracy-Widom law F 2 . Namely,

N 2/3 (λ N -2) → F 2
in distribution as N goes to infinity. They proved this result for Gaussian matrices of the GUE, later extended to families of non-Gaussian Wigner matrices by Soshnikov (see [START_REF] Soshnikov | Universality at the edge of the spectrum in Wigner random matrices[END_REF]). Recent results by Tao and Vu (see [START_REF] Tao | Random matrices: universality of local eigenvalue statistics up to the edge[END_REF]) and by Erdös, Yau and Yin (see [START_REF] Erdös | Rigidity of eigenvalues of generalized Wigner matrices[END_REF]) provide alternate proofs of this fact for larger families of Wigner matrices. According to this asymptotic property, the variance of the largest eigenvalue λ N is thus of the order of N -4/3 . In the bulk, Gustavsson proved in [START_REF] Gustavsson | Gaussian fluctuations of eigenvalues in the GUE[END_REF] again for the GUE, that, for any fixed η > 0 and all ηN j (1η)N,

λ j -γ j 2 log N (4-γ 2 j )N 2 → N (0, 1) (1)
in distribution as N goes to infinity. This result was extended by Tao and Vu in [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF] to large families of non-Gaussian Wigner matrices. The variance of an eigenvalue λ j in the bulk is thus of the order of log N N 2 . Right-side intermediate eigenvalues consist in the λ j 's with N 2 j N such that j N → 1 but Nj → ∞ as N goes to infinity (the left-side can be deduced by symmetry). Gustavsson proved a Central Limit Theorem for these eigenvalues (see [START_REF] Gustavsson | Gaussian fluctuations of eigenvalues in the GUE[END_REF]) from which their variance is guessed to be of the order of log(N -j) N 4/3 (N -j) 2/3 . This result was again extended to large classes of Wigner matrices by Tao and Vu in [START_REF] Tao | Random matrices: universality of local eigenvalue statistics up to the edge[END_REF].

The previous results are however asymptotic. As announced, the purpose of this work is to provide quantitative bounds on the variance of the eigenvalues of the correct order, in the bulk and at the edge of the spectrum, as well as for some intermediate eigenvalues. In the statements below, M N is a complex (respectively real) Wigner matrix satisfying condition (C0). This condition, which will be detailed in Section 2.1, provides an exponential decay of the matrix entries. Assume furthermore that the entries of M N have the same first four moments as the entries of a GUE matrix (respectively GOE). Set

W N = 1 √ N M N .
Theorem 1 (in the bulk). For all 0 < η 1 2 , there exists a constant C(η) > 0 such that, for all N 2 and for all ηN j (1η)N,

Var(λ j ) C(η) log N N 2 . ( 2
)
Theorem 2 (at the edge). There exists a universal constant C > 0 such that, for all N 1, Var(λ 1 ) CN -4/3 and Var(λ N ) CN -4/3 .

(

) 3
Theorem 3 (between the bulk and the edge). For all 0 < η 1 2 and for all K > 20 √ 2, there exists a constant C(η, K) > 0 such that, for all N 2, for all K log N j ηN and (1η)N j N -K log N, Var(λ j ) C(η, K) log min(j, Nj)

N 4/3 min(j, N -j) 2/3 . (4)
It should be mentioned that Theorem 1 does not seem to be known even for Gaussian matrices. The first step is thus to prove it for the GUE. This will be achieved via the analysis of the eigenvalue counting function, which due to the particular determinantal structure in this case, has the same distribution as a sum of independent Bernoulli variables. Sharp standard deviation inequalities are thus available in this case. These may then be transferred to the eigenvalues in the bulk together with Gustavsson's bounds on the variance of the eigenvalue counting function. As a result, we actually establish that

E |λ j -γ j | 2 C(η) log N N 2
leading thus to Theorem 1 in this case. Similarly, Theorem 3 does not seem to be known for Gaussian matrices. The proof follows exactly the same scheme and we establish that

E |λ j -γ j | 2 C(η, K) log min(j, N -j) N 4/3 min(j, N -j) 2/3 .
On the other hand, Theorem 2 for the GUE and GOE has been known for some time (see [START_REF] Ledoux | Small deviations for β-ensembles[END_REF]). On the basis of these results for the GUE (and GOE), Theorems 1, 2 and 3 are then extended to families of Wigner matrices by a suitable combination of Tao and Vu's Four Moment Theorem (see [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF], [START_REF] Tao | Random matrices: universality of local eigenvalue statistics up to the edge[END_REF]) and Erdös, Yau and Yin's Localization Theorem (see [START_REF] Erdös | Rigidity of eigenvalues of generalized Wigner matrices[END_REF]). The basic idea is that while the localization properties almost yield the correct order, the Four Moment Theorem may be used to reach the optimal bounds by comparison with the Gaussian models. Theorems 1, 2 and 3 are established first in the complex case. The real case is deduced by means of interlacing formulas. Furthermore, analogous results are established for covariance matrices, for which non-asymptotic quantitative results are needed, as they are useful in several fields, such as compressed sensing (see [START_REF] Vershynin | Introduction to the non-asymptotic analysis of random matrices[END_REF]), wireless communication and quantitative finance (see [START_REF] Bai | Spectral analysis of large dimensional random matrices[END_REF]). The method developed here do not seem powerful enough to strengthen the variance bounds into exponential tail inequalities. In the recent contribution [START_REF] Tao | Random matrices: sharp concentration of eigenvalues, submitted[END_REF], Tao and Vu proved such exponential tail inequalities by a further refinement of the replacement method leading to the Four Moment Theorem. While much more powerful than variance bounds, they do not seem to yield at this moment the correct order of the variance bounds of Theorems 1, 2 and 3.

As a corollary of the latter results, a bound on the rate of convergence of the empirical spectral measure L N can be achieved. This bound is expressed in terms of the so-called 2-Wasserstein distance W 2 between L N and the semicircle law ρ sc . For p ∈ [1, ∞), the p-Wasserstein distance W p (µ, ν) between two probability measures µ and ν on R is defined by

W p (µ, ν) = inf R 2 |x -y| p dπ(x, y) 1/p
where the infimum is taken over all probability measure π on R 2 such that its first marginal is µ and its second marginal is ν. Note for further purposes that the rate of convergence of this empirical distribution has been investigated in various forms. For example, the Kolmogorov distance between L N and ρ sc has been considered in this respect in several papers (see for example [START_REF] Götze | Rate of convergence to the semicircular law[END_REF], [START_REF] Bobkov | On concentration of empirical measures and convergence to the semi-circle law[END_REF] and [START_REF] Götze | On the rate of convergence to the semicircular law[END_REF]). It is given by

d K (L N , ρ sc ) = sup x∈R 1 N N x -G(x) ,
where N x is the eigenvalue counting function and G is the distribution function of the semicircle law. More and more precise bounds were established. For example, under the hypothesis of an exponential decay of the matrix entries, Götze and Tikhomirov recently showed that, with high probability,

d K (L N , ρ sc ) (log N) c
N for some universal constant c > 0 (see [START_REF] Götze | On the rate of convergence to the semicircular law[END_REF]). The rate of convergence in terms of W 1 , also called the Kantorovich-Rubinstein distance, was studied by Guionnet and Zeitouni in [START_REF] Guionnet | Concentration of the spectral measure for large matrices[END_REF] and recently by Meckes and Meckes in [START_REF] Meckes | Concentration and convergence rates for spectral measure of random matrices[END_REF]. In [START_REF] Meckes | Concentration and convergence rates for spectral measure of random matrices[END_REF], the authors

proved that [W 1 (L N , [L N ])]
CN -2/3 . The following is concerned with the distance between L N and ρ sc and strengthens the preceding conclusion on W 1 .

Corollary 4. There exists a numerical constant C > 0 such that, for all N 2,

W 2 2 (L N , ρ sc ) C log N N 2 . (5)
The proof of this corollary relies on the fact that W 2 2 (L N , ρ sc ) is bounded above, up to a constant, by the sum of the expectations (λ j -γ j ) 2 . The previously established bounds then easily yield the result.

Turning to the organization of the paper, Section 1 describes Theorems 1, 2 and 3 in the GUE case. Section 2 emphasizes to start with the Four Moment Theorem of Tao and Vu (see [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF] and [START_REF] Tao | Random matrices: universality of local eigenvalue statistics up to the edge[END_REF]) and the Localization Theorem of Erdös, Yau and Yin (see [START_REF] Erdös | Rigidity of eigenvalues of generalized Wigner matrices[END_REF]). On the basis of these results and the GUE case, the main Theorems 1, 2 and 3 are then established for families of Wigner matrices. Section 3 is devoted to the corresponding statements for real matrices, while Section 5 describes the analogous results for covariance matrices. Section 4 deals with Corollary 4 and the rate of convergence of L N towards ρ sc in terms of 2-Wasserstein distance.

Throughout this paper, C(•) and c(•) will denote positive constants which depend on the specified quantities and may differ from one line to another.

Deviation inequalities and variance bounds in the GUE case

The results and proofs developped in this section for the GUE model heavily rely on its determinantal structure which allows for a complete description of the joint law of the eigenvalues (see [START_REF] Anderson | An introduction to random matrices[END_REF]). In particular the eigenvalue counting function is known to have the same distribution as a sum of independent Bernoulli variables (see [START_REF] Hough | Determinantal processes and independence[END_REF], [START_REF] Anderson | An introduction to random matrices[END_REF]), whose mean and variance were computed by Gustavsson (see [START_REF] Gustavsson | Gaussian fluctuations of eigenvalues in the GUE[END_REF]). Deviation inequalities for individual eigenvalues can thus be established, leading to the announced bounds on the variance.

Eigenvalues in the bulk of the spectrum

The aim of this section is to establish the following result.

Theorem 5. Let M N be a GUE matrix. Set W N = 1 √ N M N . For any 0 < η 1 2
, there exists a constant C(η) > 0 such that for all N 2 and all ηN j (1η)N,

|λ j -γ j | 2 C(η) log N N 2 . ( 6
)
In particular,

Var(λ j ) C(η) log N N 2 . ( 7
)
As announced, the proof is based on the connection between the distribution of eigenvalues and the eigenvalue counting function. For every t ∈ R, let N t = N i=1 ½ λ i t be the eigenvalue counting function. Due to the determinantal structure of the GUE, it is known (see [START_REF] Hough | Determinantal processes and independence[END_REF]) that N t has the same distribution as a sum of independent Bernoulli random variables. Bernstein's inequality for example (although other, even sharper, inequalities may be used) may then be applied to get that for every u 0,

È N t -[N t ] u 2 exp - u 2 2σ 2 t + u , (8)
where σ 2 t is the variance of N t (see for example [START_REF] Van Der | Asymptotic statistics[END_REF]). Note that the upper-bound is non-increasing in u while non-decreasing in the variance. Set for simplicity ρ t = ρ sc (-∞, t] , t ∈ R. It has been shown in [START_REF] Götze | The rate of convergence for spectra of GUE and LUE matrix ensembles[END_REF] that for some numerical constant

C 1 > 0, sup t∈R [N t ] -Nρ t C 1 .
In particular thus, together with (8), for every u 0,

È N t -Nρ t u + C 1 2 exp - u 2 2σ 2 t + u . (9)
As a main conclusion of the work by Gustavsson [START_REF] Gustavsson | Gaussian fluctuations of eigenvalues in the GUE[END_REF], for every δ ∈ (0, 2), there exists c δ > 0 such that sup

t∈I δ σ 2 t c δ log N, (10)
where

I δ = [-2+δ, 2-δ].
On the basis of inequalities ( 9) and [START_REF] Götze | On the rate of convergence to the semicircular law[END_REF], it is then possible to derive a deviation inequality for eigenvalues λ j in the bulk from their theoretical locations γ j ∈ [-2, 2], 1 j N, defined by ρ γ j = j N .

Proposition 6 (Deviation inequality for λ j ). Let η ∈ (0, 1 2 ] and ηN j (1-η)N. There exist C > 0, c > 0, c ′ > 0 and δ ∈ (0, 2) (all depending on η) such that, for all c u c ′ N,

È |λ j -γ j | u N 4 exp - C 2 u 2 2c δ log N + Cu . ( 11
)
Proof. Let η ∈ (0, 1 2 ]. To start with, evaluate, for N 2 j (1η)N and u 0, the probability È(|λ jγ j | > u N ). We have

È λ j > γ j + u N = È N i=1 ½ λ i γ j + u N < j = È N γ j + u N < j = È Nρ γ j + u N -N γ j + u N > Nρ γ j + u N -j = È Nρ γ j + u N -N γ j + u N > N(ρ γ j + u N -ρ γ j )
where it has been used that ρ γ j = j N . Then

È λ j > γ j + u N È |N γ j + u N -Nρ γ j + u N | > N(ρ γ j + u N -ρ γ j ) . But ρ γ j + u N -ρ γ j = γ j + u N γ j 1 2π √ 4 -x 2 dx 1 √ 2π γ j + u N γ j √ 2 -x dx √ 2 3π (2 -γ j ) 3/2 1 -1 - u N 2 -γ j 3/2 √ 2 3π (2 -γ j ) 1/2 u N , if u (2 -γ j )N. By definition, 1 -j N = 2 γ j 1 2π √ 4 -x 2 dx. Then 1 √ 2π 2 γ j √ 2 -x dx 1 - j N 1 π 2 γ j √ 2 -x dx.
Computing the preceding integrals yields

3π 2 N -j N 2/3 2 -γ j 3π √ 2 N -j N 2/3 . ( 12
) Therefore, u (2 -γ j )N if u c ′ N with c ′ ( 3π 2 ) 2/3 η 2/3
. In this case, (12) yields

ρ γ j + u N -ρ γ j 2C u N
where C > 0. Therefore

È λ j > γ j + u N È |N γ j + u N -Nρ γ j + u N | > 2Cu È |N γ j + u N -Nρ γ j + u N | > Cu + C 1 when u C 1 C = c. Then, applying (9) leads to È λ j > γ j + u N 2 exp - C 2 u 2 2σ 2 γ j + u N + Cu .
As γ j is in the bulk, there exist δ and c ′ < ( 3π 2 ) 2/3 η 2/3 such that γ j + u N ∈ I δ , for all ηN j (1η)N and for all c u c ′ N, for all N 1 (both δ and c ′ depend on η). Then

È λ j > γ j + u N 2 exp - C 2 u 2 2c δ log N + Cu .
Repeating the argument leads to the same bound on È λ j < γ j -u N . Therefore,

È |λ j -γ j | u N 4 exp - C 2 u 2 2c δ log N + Cu .
The proposition is thus established.

Proof of Theorem 5. Note first that, for every j,

λ 4 j N i=1 λ 4 i = Tr(W 4 N ) .
The mean of this trace can be easily computed and is equal to 2N +

1 N . Consequently, for all N 1, λ 4 j 3N. ( 13
) Choose next M = M(η) > 0 large enough such that C 2 M 2 2c δ +CM > 5. Setting Z = N|λ j -γ j |, [Z 2 ] = ∞ 0 È(Z v)2v dv = c 0 È(Z v)2v dv + M log N c È(Z v)2v dv + ∞ M log N È(Z v)2v dv c 2 + I 1 + I 2 .
The two latter integrals are handled in different ways. The first one I 1 is bounded using (11) while I 2 is controlled using the Cauchy-Schwarz inequality and (13). Starting thus with I 2 ,

I 2 = +∞ M log N È(Z v)2v dv Z 2 ½ Z M log N Z 4 È Z M log N 2A exp 1 2 5 - C 2 M 2 2c δ + CM log N ,
where A > 0 is a numerical constant. As exp

1 2 5 -C 2 M 2 2c δ +CM log N → N →∞
0, there exists a constant C(η) > 0 such that

I 2 C(η).
Turning to I 1 , recall that Proposition 6 gives, for c v c ′ N,

P (Z v) = È |λ j -γ j | v N 4 exp - C 2 v 2 2c δ log N + Cv .
Hence in the range v M log N,

P (Z v) 4 exp - B log N v 2 ,
where B = B(η) = C 2 2c δ +CM . There exists thus a constant C(η) > 0 such that

I 1 C(η) log N.
Summarizing the previous steps, Z 2 C(η) log N. Therefore

|λ j -γ j | 2 C(η) log N N 2
, which is the claim. The proof of Theorem 5 is complete.

It may be shown similarly that, under the assumptions of Theorem 5,

|λ j -γ j | p C(p, η) (log N) p/2 N p .

Eigenvalues at the edge of the spectrum

In [START_REF] Ledoux | Small deviations for β-ensembles[END_REF], Ledoux and Rider gave unified proofs of precise small deviation inequalities for the extreme eigenvalues of β-ensembles. The results hold in particular for GUE matrices (β = 2) and for GOE matrices (β = 1). The following theorem summarizes some of the relevant inequalities for the GUE.

È λ N 2(1 + ε) C exp - 2N C ε 3/2 , ( 14
)
and

È λ N 2(1 -ε) C 2 exp - 2N 2 C ε 3 . ( 15
)
There exists also a right-tail large deviation inequality for ε = O(1) of the form

È λ N 2(1 + ε) C exp - 2N C ε 2 , ( 16
)
where C > 0 is a universal constant. Similar inequalities hold for the smallest eigenvalue λ 1 . As stated in [START_REF] Ledoux | Small deviations for β-ensembles[END_REF], bounds on the variance straightly follow from these deviation inequalities.

Corollary 8. Let M N be a GUE matrix. Set W N = 1 √ N M N .
Then there exists a universal constant C > 0 such that for all N 1,

Var(λ N ) (λ N -2) 2 CN -4/3 .
Similar results are probably true for the k th smallest or largest eigenvalue (for k ∈ N fixed).

Eigenvalues between the bulk and the edge of the spectrum

The aim of this section is to establish a bound on the variance for some intermediate eigenvalues. The proof is very similar to what was done for eigenvalues in the bulk. It relies on the fact that the eigenvalue counting function has the same distribution as a sum of independent Bernoulli variables due to the determinantal properties of the eigenvalues. A deviation inequality for individual eigenvalues can then be derived and the bound on the variance straightly follows. Parts of the proof which are identical to the proof for eigenvalues in the bulk will be omitted. In what follows we only consider the right-side of the spectrum. Results and proofs for the left-side can be deduced by replacing Nj by j. The precise statement is the following.

Theorem 9. Let M N be a GUE matrix. Set W N = 1 √ N M N . For all K 20 √
2 and for all η ∈ (0, 1 2 ], there exists a constant C(η, K) > 0 such that for all N 2 and all (1η)N j N -K log N,

|λ j -γ j | 2 C(η, K) log(N -j) N 4/3 (N -j) 2/3 . ( 17
)
In particular,

Var(λ j ) C(η, K) log(N -j) N 4/3 (N -j) 2/3 . (18)
The preceding theorem does not concern all intermediate eigenvalues since Nj has to be at least of the order of 20 √ 2 log N for the method used here to yield the correct order on the variance. This restriction seems however to be technical since Gustavsson [START_REF] Gustavsson | Gaussian fluctuations of eigenvalues in the GUE[END_REF] proved that a Central Limit Theorem holds for all eigenvalues λ j such that Nj → ∞ but N -j N → 0. From this CLT, the variance of such an individual eigenvalue λ j is guessed to be similarly of the order of log(N -j) N 4/3 (N -j) 2/3 for this range.

As for eigenvalues in the bulk, the proof relies on the deviation inequality for the eigenvalue counting function [START_REF] Forrester | Inter-relationships between orthogonal, unitary and symplectic matrix ensembles[END_REF]. From this and a bound on the variance of this counting function [START_REF] Götze | On the rate of convergence to the semicircular law[END_REF], it was then possible to derive a deviation inequality for eigenvalues in the bulk. The work of Gustavsson [START_REF] Gustavsson | Gaussian fluctuations of eigenvalues in the GUE[END_REF] suggests that for all 0 < η < 4 and for all K > 0, there exists a constant c η, K > 0 such that the following holds. For every sequence (t N ) N ∈N such that, for all N, 0 < 2t N η and N(2

-t N ) 3/2 K log N, σ 2 t N c η, K log N(2 -t N ) 3/2 . ( 19
)
Similarly to the bulk case, the following proposition can be established.

Proposition 10 (Deviation inequality for intermediate λ j

). There exist universal positive constants C and c such that the following holds. Let K > 20 √ 2 and η ∈ (0, 1 2 ]. Set (1η)N j N -K log N. There exists C ′ > 0 and c ′ > 0 depending on K and η such that for all c u c ′ (Nj),

È |λ j -γ j | u N 2/3 (N -j) 1/3 4 exp - C 2 u 2 C ′ log(N -j) + Cu . ( 20
)
Proof. Set C = 2 -5/6 (3π) -2/3 . Let K > 20 √ 2 and η ∈ (0, 1 2 ]. Take α ∈ 20

√ 2 K , 1 and set c ′ = α 3π 2 2/3 . For (1-η)N j N -K log N and u 0, set u N,j = u N 2/3 (N -j) 1/3 .
As in the proof of Proposition 6, evaluating the probability È(|λ j -γ j | > u N,j ) yields

È λ j > γ j + u N,j È |N γ j +u N,j -Nρ γ j +u N,j | > N(ρ γ j +u N,j -ρ γ j ) . But, if u N 2/3 (N -j) 1/3 (2 -γ j ), ρ γ j +u N,j -ρ γ j √ 2 3π
(2γ j ) 1/2 u N,j .

Similarly to the proof of Proposition 6, this condition holds if u c ′ (Nj). In this case,

È λ j > γ j + u N,j È |N γ j +u N,j -Nρ γ j +u N,j | > Cu + C 1 , when u c = C 1 C .
Then, applying (9) leads to

È λ j > γ j + u N,j 2 exp - C 2 u 2 2σ 2
γ j +u N,j + Cu for all c u c ′ (Nj). Gustavsson's result [START_REF] Pastur | Eigenvalue distribution of large random matrices[END_REF] gives a bound on σ 2 γ j +u N,j . Set

t N = γ j + u N,j . As j (1 -η)N and u 0, 0 2 -t N 2 -γ j 3π 2 η 2/3 = η for all N. Moreover, N(2 -t N ) 3/2 = N(2 -γ j -u N,j ) 3/2 = N(2 -γ j ) 3/2 1 - u N,j 2 -γ j 3/2 3π 2 (N -j) 1 - c ′ (N -j) 2/3 N 2/3 (2 -γ j ) 3/2 3π 2 (1 -α) 3/2 K log N K log N,
where K = 3π 2 (1α) 3/2 K > 0. From [START_REF] Pastur | Eigenvalue distribution of large random matrices[END_REF], for all c u c ′ (Nj),

Var(N γ j +u N,j ) c η, K log N(2 -t N ) 3/2 . But N(2 -t N ) 3/2 = N(2 -γ j -u N,j ) 3/2 N(2 -γ j ) 3/2 3π √ 2 (N -j). Hence log N(2 -t N ) 3/2 log(N -j) + log( 3π √ 2 ). As K > 20 √ 2 and N 2, N -j K log N 3π √ 2 and Var(N γ j +u N,j ) 2c η, K log(N -j). Therefore È λ j > γ j + u N,j 2 exp - C 2 u 2 4c η, K log(N -j) + Cu .
The proof is concluded similarly to Proposition 6.

On the basis of Proposition 10, we may then conclude the proof of Theorem 9.

Proof of Theorem 9. Setting Z = N 2/3 (Nj) 1/3 |λ jγ j |,

[Z 2 ] = ∞ 0 È(Z v)2v dv = c 0 È(Z v)2v dv + C ′ C log(N -j) c È(Z v)2v dv + c ′ (N -j) C ′ C log(N -j) È(Z v)2v dv + ∞ c ′ (N -j) È(Z v)2v dv c 2 + J 1 + J 2 + J 3 .
Repeating the computations carried out with I 2 in the proof of Theorem 5 yields

J 3 2AN 11/6 (N -j) 2/3 exp - 1 2 C 2 c ′2 (N -j) 2 C ′ log(N -j) + Cc ′ (N -j) ,
where A > 0 is a numerical constant. For N large enough (depending on η and K), C ′ log(Nj) Cc ′ (Nj) and

J 3 2AN 5/2 exp - Cc ′ 4 (N -j) .
Then, as Nj K log N,

J 3 2AN 5/2 exp - KCc ′ 4 log N . But KCc ′ 4 = Kα 8 √ 2 > 5 2 .
The right-hand side goes thus to 0 when N goes to infinity. As a consequence, there exists a constant C(η, K) > 0 such that

J 3 C(η, K).
The integral J 1 is handled as I 1 , using that, in the range v C ′ C log(Nj),

P (Z v) 4 exp - B log(N -j) v 2 ,
where B = B(η, K) = C 2 2C ′ (this is due to Proposition 10). Hence, there exists a constant C(η, K) such that

J 1 C(η, K) log(N -j).
Finally, J 2 is handled similarly. From Proposition 10, in the range

C ′ C log(N -j) v c ′ (N -j), P (Z v) 4 exp - C 2 v .
Thus

J 2 4 c ′ (N -j) C ′ C log(N -j) exp - C 2 v 2v dv 4 ∞ 0 exp - C 2 v 2v dv.
Then J 2 is bounded by a constant, which is independent of η and K. There exists thus a constant C > 0 such that J 2 C.

Summarizing the previous steps, Z 2 C(η, K) log(Nj). Therefore

|λ j -γ j | 2 C(η, K) log(N -j) N 2/3 (N -j) 1/3 ,
which is the claim.

Variance bounds for Wigner Hermitian matrices

As announced, the goal of this section is to prove Theorems 1, 2 and 3 for Wigner Hermitian matrices. The eigenvalues of a Wigner Hermitian matrix do not form a determinantal process. Therefore it does not seem easy to provide deviation inequalities for the counting function and for individual eigenvalues. However the sharp non-asymptotic bounds established in the Gaussian case can still be reached by a comparison procedure.

Localization of the eigenvalues and the Four Moment Theorem

Two main recent theorems will be used in order to carry out this comparison procedure. First, Erdös, Yau and Yin proved in [START_REF] Erdös | Rigidity of eigenvalues of generalized Wigner matrices[END_REF] a Localization Theorem which gives a high probability non-asymptotic bound on the distance between an eigenvalue λ j and its theoretical value γ j . Secondly, Tao and Vu's Four Moment Theorem (see [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF] and [START_REF] Tao | Random matrices: universality of local eigenvalue statistics up to the edge[END_REF]) provides a very useful non-asymptotic bound on the error made by approximating a statistics of the eigenvalues of a Wigner matrix by the same statistics but with the eigenvalues of a GUE matrix. Let M N be a Wigner Hermitian matrix. Say that M N satisfies condition (C0) if the real part ξ and the imaginary part ξ of (M N ) ij are independent and have an exponential decay: there are two positive constants B 1 and B 2 such that

È |ξ| t B 1 e -t and È | ξ| t B 1 e -t
for all t B 2 .

Theorem 11 (Localization [START_REF] Erdös | Rigidity of eigenvalues of generalized Wigner matrices[END_REF]). Let M N be a random Hermitian matrix whose entries satisfy condition (C0). There are positive universal constants c and C such that, for any 1 j N,

È |λ j -γ j | (log N) C log log N N -2/3 min(j, N +1-j) -1/3 Ce -(log N ) c log log N . ( 21
)
This a strong localization result and it almost yields the correct order on the bound on the variance. Indeed, by means of the Cauchy-Schwarz inequality and ( 13), it can be shown that, for ηN j (1η)N,

Var(λ j ) (λ j -γ j ) 2 C(η) N 2 (log N) C log log N .
In Tao and Vu's recent paper [START_REF] Tao | Random matrices: sharp concentration of eigenvalues, submitted[END_REF] on deviation inequalities, the authors proved a more precise localization result. They indeed established a bound similar to [START_REF] Soshnikov | Universality at the edge of the spectrum in Wigner random matrices[END_REF] but with (log N) A instead of (log N) C log log N (where A > 0 is fixed). However this more precise bound is of no help below, as the final bounds on the variances remain unchanged.

We turn now to Tao and Vu's Four Moment Theorem, in order to compare W N with a GUE matrix W ′ N . Say that two complex random variables ξ and ξ ′ match to order k if

ℜ(ξ) m ℑ(ξ) l = ℜ(ξ ′ ) m ℑ(ξ ′ ) l
for all m, l 0 such that m + l k.

Theorem 12 (Four Moment Theorem [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF], [START_REF] Tao | Random matrices: universality of local eigenvalue statistics up to the edge[END_REF]). There exists a small positive constant c 0 such that the following holds. Let M N = (ξ ij ) 1 i,j N and M ′ N = (ξ ′ ij ) 1 i,j N be two random Wigner Hermitian matrices satisfying condition (C0). Assume that, for 1 i < j N, ξ ij and ξ ′ ij match to order 4 and that, for 1 i N, ξ ii and ξ ′

ii match to order 2.

Set A N = √ NM N and A ′ N = √ NM ′ N .
Let G : R → R be a smooth fonction such that:

∀ 0 k 5, ∀x ∈ R, G (k) (x) N c 0 . ( 22
)
Then, for all 1 i N and for N large enough (depending on constants B 1 and

B 2 in condition (C0)), G(λ i (A N )) -G(λ i (A ′ N )) N -c 0 . (23)
Actually Tao and Vu proved this theorem in a more general form, involving a finite number of eigenvalues. In this work, it will only be used with one given eigenvalue. See [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF] and [START_REF] Tao | Random matrices: universality of local eigenvalue statistics up to the edge[END_REF] for more details.

It should be mentionned that Tao and Vu extended in [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF] Gustavsson's result (see equation ( 1)) via this theorem. By means of a smooth bump function G, they compared the probability for λ j to be in a given interval for a non-Gaussian matrix with almost the same probability but for a GUE matrix. Applying this technique to È |λ jγ j | > u N in order to extend directly the deviation inequality leads to the following in the general Wigner case: for all ηN j (1η)N and for all

C ′ u c ′ N, È |λ j -γ j | > u N C exp - u 2 c log N + u + O(N -c 0 ),
where C, C ′ , c and c ′ are positive constants depending only on η. The bound is not exponential anymore and is not enough to conclude towards sharp bounds on the variance or higher moments.

Comparison with Gaussian matrices

Let M N be a Hermitian Wigner matrix and M ′ N be a GUE matrix such that they satisfy the hypotheses of Theorem 12. As the function G : x ∈ R → x 2 does not satisfy [START_REF] Su | Gaussian fluctuations in complex sample covariance matrices[END_REF], Theorem 12 will be applied to a truncation of G. Theorem 11 will provide a small area around the theoretical location γ j where the eigenvalue λ j is very likely to be in, so that the error due to the truncation will be well controlled. Note that this procedure is valid for eigenvalues in the bulk and at the edge of the spectrum, as well as for intermediate eigenvalues.

Let

1 j N. Set R (j) N = (log N) C log log N N 1/3 min(j, N + 1 -j) -1/3 and ε N = Ce -(log N ) c log log N .
Then Theorem 11 leads to:

È |λ j -γ j | R (j) N N ε N . (24)
Let ψ be a smooth function with support [-2, 2] and values in [0, 1] such that

ψ(x) = 1 10 x 2 for all x ∈ [-1; 1]. Set G j : x ∈ R → ψ x-N γ j R (j) N
. We want to apply Tao and Vu's Four Moment Theorem 12 to G j . As ψ is smooth and has compact support, its first five derivatives are bounded by M > 0. Then, for all 0 k 5, for all x ∈ R,

G (k) j (x) M (R (j) N ) k N c 0 ,
where the last inequality holds for N large enough (depending only on M and c 0 ). Then, the Four Moment Theorem 12 yields:

G j (λ j (A N )) -G j (λ j (A ′ N )) N -c 0 (25)
for large enough N. But

G j (λ j (A N )) = 1 10 λ j (A N )-N γ j R (j) N 2 ½ |λ j (A N )-Nγ j | R (j) N 1 + G j (λ j (A N ))½ |λ j (A N )-Nγ j | R (j) N >1 = N 2 10 R (j) N 2 (λ j -γ j ) 2 ½ |λ j -γ j | R (j) N N + G j (λ j (A N ))½ |λ j (A N )-Nγ j | R (j) N >1 .
On the one hand,

G j (λ j (A N ))½ |λ j -Nγ j | R (j) N >1 È |λ j (W N ) -γ j | > R (j) N N ε N .
On the other hand,

(λ j -γ j ) 2 ½ |λ j -γ j | R (j) N N = (λ j -γ j ) 2 -(λ j -γ j ) 2 ½ |λ j -γ j |> R (j) N N
.

From the Cauchy-Schwarz inequality and ( 13),

(λ j -γ j ) 2 ½ |λ j -γ j |> R (j) N N (λ j -γ j ) 4 È |λ j -γ j | > R (j) N N A Nε N
and there exists a constant C(η, K) > 0 such that for all (1-η)N j N -K log N (and similarly for the left-side of the spectrum),

Var(λ j ) (λ j -γ j ) 2 C(η, K) log(N -j) N 4/3 (N -j) 2/3 .

A corollary on the 2-Wasserstein distance

The bounds on the variances, more exactly on [(λ jγ j ) 2 ], developed in the preceding sections lead to a bound on the rate of convergence of the empirical spectral measure L N towards the semicircle law ρ sc in terms of 2-Wasserstein distance. Recall that W 2 (L N , ρ sc ) is a random variable defined by

W 2 (L N , ρ sc ) = inf R 2 |x -y| 2 dπ(x, y) 1/2
, where the infimum is taken over all probability measures π on R 2 with respective marginals L N and ρ sc . To achieve the expected bound, we rely on another expression of W 2 in terms of distribution functions, namely

W 2 2 (L N , ρ sc ) = 1 0 F -1 N (x) -G -1 (x) 2 dx, (29)
where F -1 N (respectively G -1 ) is the generalized inverse of the distribution function F N (respectively G) of L N (respectively ρ sc ) (see for example [START_REF] Villani | Topics in optimal transportation[END_REF]). On the basis of this representation, the following statement may be derived. Proposition 13. There exists a universal constant C > 0 such that for all N 1,

W 2 2 (L N , ρ sc ) 2 N N j=1 (λ j -γ j ) 2 + C N 2 . ( 30
)
Proof. From (29),

W 2 2 (L N , ρ sc ) = 1 0 F -1 N (x) -G -1 (x) 2 dx.
Then,

W 2 2 (L N , ρ sc ) = N j=1 j N j-1 N λ j -G -1 (x) 2 dx 2 N N j=1 (λ j -γ j ) 2 + 2 N j=1 j N j-1 N γ j -G -1 (x) 2 dx.
But γ j = G -1 j N and G -1 is non-decreasing. Therefore, γ j -G -1 (x) γ jγ j-1 for all x ∈ j-1 N , j N . Consequently,

W 2 2 (L N , ρ sc ) 2 N N j=1 (λ j -γ j ) 2 + 2 N N j=1 (γ j -γ j-1 ) 2 . ( 31
) But if j -1 N 2 (and therefore γ j-1 0), 1 N = γ j γ j-1 1 2π √ 4 -x 2 dx 1 √ 2π γ j γ j-1 √ 2 -x dx √ 2 3π (2 -γ j-1 ) 3/2 1 -1 - γ j -γ j-1 2 -γ j-1 3/2 √ 2 3π (2 -γ j-1 ) 1/2 (γ j -γ j-1 ) √ 2 3π 3π 2 N -j + 1 N 1/3
(γ jγ j-1 ), from [START_REF] Götze | The rate of convergence for spectra of GUE and LUE matrix ensembles[END_REF]. Then

γ j -γ j-1 (3π) 2/3 2 -1/6 N 2/3 (N -j + 1) 2/3 .
It may be shown that a similar bound holds if j -1 N 2 . As a summary, there exists a universal constant c > 0 such that, for all j 2,

γ j -γ j-1 c N 2/3 min(j, N + 1 -j) 1/3 . (32)
This yields

N j=1 (γ j -γ j-1 ) 2 c 2 N 4/3 N j=1 1 min(j, N + 1 -j) 2/3 C N ,
where C > 0 is a universal constant. Then (31) becomes

W 2 2 (L N , ρ sc ) 2 N N j=1 (λ j -γ j ) 2 + C N 2 ,
where C > 0 is a universal constant, which is the claim.

Proof of Corollary 4. Let N 2. Due to Proposition 13,

W 2 2 (L N , ρ sc ) 2 N N j=1 (λ j -γ j ) 2 + C N 2 .
We then make use of the bounds on (λ jγ j ) 2 produced in the previous sections.

Set η ∈ (0, 1 2 ] and K > 20 √ 2 so that K log N ηN. We first decompose

N j=1 (λ j -γ j ) 2 = K log N j=1 (λ j -γ j ) 2 + ηN j=K log N +1 (λ j -γ j ) 2 + (1-η)N -1 j=ηN +1 (λ j -γ j ) 2 + N -K log N -1 j=(1-η)N (λ j -γ j ) 2 + N j=N -K log N (λ j -γ j ) 2 = Σ 1 + Σ 2 + Σ 3 + Σ 4 + Σ 5 .
The sum Σ 3 will be bounded using the bulk case (Theorem 1), while Theorem 2 will be used to handle Σ 2 and Σ 4 . A crude version of Theorem 11 will be enough to bound Σ 1 and Σ 5 . To start with thus, from Theorem 1,

Σ 3 (1-η)N -1 j=ηN +1 C(η) log N N 2 C(η) log N N .
Secondly, from Theorem 3,

È |λ j -γ j | 1 √ N Ce -(log N ) c log log N .
Then, by the Cauchy-Schwarz inequality,

(λ j -γ j ) 2 (λ j -γ j ) 2 ½ |λ j -γ j | 1 √ N + (λ j -γ j ) 2 ½ |λ j -γ j |> 1 √ N 1 N + |λ j -γ j | 4 È |λ j -γ j | > 1 √ N 1 N + √ 3CN 1/2 e -(log N ) c log log N . As √ 3CN 1/2 e -(log N ) c log log N = o( 1 N ), there exists a constant C > 0 such that (λ j - γ j ) 2 C N . Then Σ 1 + Σ 5 2KC log N N . As a consequence, N j=1 (λ j -γ j ) 2 C log N N . Therefore W 2 (L N , ρ sc ) 2 C log N N 2
, where C > 0 is a universal constant, which is the claim. The corollary is thus established.

The preceding Corollary 4 implies that

W 1 (L N , ρ sc ) C √ log N N .
This is an improvement of Meckes and Meckes' rate of convergence obtained in [START_REF] Meckes | Concentration and convergence rates for spectral measure of random matrices[END_REF]. Note however that the distance studied in [START_REF] Meckes | Concentration and convergence rates for spectral measure of random matrices[END_REF] is the expected 1-Wasserstein distance between L N and its mean instead of ρ sc . The rate of convergence in 1-Wasserstein distance can be furthermore compared to the rate of convergence in Kolmogorov distance. Indeed, if µ and ν are two probability measures on R such that ν has a bounded density function with respect to Lebesgue measure, d K (µ, ν) c W 1 (µ, ν), where c > 0 depends on the bound on the density function. This is due to the fact that

W 1 (µ, ν) = sup f 1-Lipschitz R f dµ - R f dν
(see for example [START_REF] Villani | Topics in optimal transportation[END_REF]) and d K (µ, ν) = sup x∈R µ (-∞, x]ν (-∞, x] . Approximating ½ (-∞,x] from above and from below by 1 ε -Lipschitz functions and optimizing on ε gives the result. Therefore, the preceding implies that

d K (L N , ρ sc ) c (log N) 1/4 N 1/2
which is however far from Götze and Tikhomirov's recent bound [START_REF] Götze | On the rate of convergence to the semicircular law[END_REF] d K (L N , ρ sc ) (log N) c N .

Eigenvalue variance bounds for covariance matrices

This section provides the analogous non-asymptotic bounds on the variance of eigenvalues for covariance matrices. The proofs will be detailed in another redaction. Therefore, this section contains only the background and the results. Random covariance matrices are defined by the following. Let X be a m×n (real or complex) matrix, with m n, such that its entries are independent, centered and have variance 1. Then S m,n = 1 n X * X is a random covariance matrix. An important example is the case when the entries of X are Gaussian. Then S m,n belongs to the so-called Laguerre Unitary Ensemble (LUE) if the entries of X are complex and to the Laguerre Orthogonal Ensemble (LOE) if they are real. S m,n is Hermitian (or real symmetric) and therefore has n real eigenvalues. As m n, none of these eigenvalues is trivial. Furthermore, these eigenvalues are nonnegative and will be denoted by 0

λ 1 • • • λ n .
Similarly to Wigner's Theorem, the classical Marchenko-Pastur theorem states that, if m n → ρ 1 when n goes to infinity, the empirical spectral measure L m,n =

1 n n j=1 δ λ j converges almost surely to a deterministic measure, called the Marchenko-Pastur distribution of parameter ρ. This measure is compactly supported and is absolutely continuous with respect to Lebesgue measure, with density

dµ M P (ρ) (x) = 1 2πx b ρ -x x -a ρ ½ [aρ ,bρ] (x)dx,
where a ρ = (1 -√ ρ) 2 and b ρ = (1 + √ ρ) 2 (see for example [START_REF] Bai | Spectral analysis of large dimensional random matrices[END_REF]). Two different behaviors arise according to the value of ρ. If ρ > 1, a ρ > 0 is a soft edge, which means that an eigenvalue λ j can be larger or smaller than a ρ . On the contrary, if ρ = 1, a ρ = 0 is called a hard edge: no eigenvalue can be less than a ρ . Furthermore, the Marchenko-Pastur density function explodes at 0. It is the case in particular when m = n. We will denote by µ m,n the approximate Marchenko-Pastur distribution whose density is defined by

µ m,n (x) = 1 2πx (x -a m,n )(b m,n -x)½ [am,n ; bm,n] (x),
with a m,n = (1 -m n ) 2 and b m,n = (1 + m n ) 2 . The asymptotic behaviors of individual eigenvalues for LUE matrices have been known for some time and extended to more general covariance matrices in the last decade. For an eigenvalue in the bulk of the spectrum, i.e. λ j such that ηn j (1η)n, a Central Limit Theorem was proved by Su (see [START_REF] Su | Gaussian fluctuations in complex sample covariance matrices[END_REF]) and Tao-Vu (see [START_REF] Tao | Random covariance matrices: universality of local statistics of eigenvalues[END_REF]). From this theorem, the variance of such eigenvalues is guessed to be of the order of log n n 2 . For right-side intermediate eigenvalues, i.e. λ j such that j n → 1 and nj → ∞, Su, and later Tao-Vu and Wang, proved a CLT (see [START_REF] Su | Gaussian fluctuations in complex sample covariance matrices[END_REF], [START_REF] Tao | Random covariance matrices: universality of local statistics of eigenvalues[END_REF] and [START_REF] Wang | Random covariance matrices: universality of local statistics of eigenvalues up to the edge[END_REF]). The variance appears to be of the order of log(n-j) n 4/3 (n-j) 2/3 . Similar results probably hold for the left-side of the spectrum, when ρ > 1. Finally, for the smallest and the largest eigenvalues λ 1 and λ n , CLTs were proved for Gaussian matrices by Borodin-Forrester (see [START_REF] Borodin | Increasing subsequences and the hard-to-soft edge transition in matrix ensembles[END_REF]). Several authors extended these results to more general covariance matrices. The latest results are due to Tao-Vu and Wang (see [START_REF] Tao | Random covariance matrices: universality of local statistics of eigenvalues[END_REF] and [START_REF] Wang | Random covariance matrices: universality of local statistics of eigenvalues up to the edge[END_REF]). Their variances are then guessed to be of the order of n -4/3 . It should be mentionned that the result for the smallest eigenvalue only holds when ρ > 1. When ρ = 1, in the case of a squared matrix, Edelman proved a CLT for the smallest eigenvalue λ 1 , extended by Tao and Vu in [START_REF] Tao | Random matrices: the distribution of the smallest singular values[END_REF], from which the variance is guessed to be of the order of n -4 .

The following statement summarizes a number of quantitative bounds on the eigenvalues of covariance matrices which are proved by methods similar to the ones developed in the preceding sections. For simplicity, we basically assume that ρ > 1. More precisely, we assume that 1 < A 1 m n A 2 (where A 1 and A 2 are fixed constants) and that S m,n is a covariance matrix whose entries have an exponential decay (condition (C0)) and have the same first four moments as those of a LUE matrix.

Theorem 14.

1. In the bulk of the spectrum. Let η ∈ (0, 1 2 ]. There exist a constant C > 0 (depending on η, A 1 and A 2 ) such that for all covariance matrix S m,n , for all ηn j (1η)n, Var(λ j ) C log n n 2 .

Between the bulk and the edge of the spectrum.

There exists a constant κ > 0 (depending on A 1 and A 2 ) such that the following holds. For all K > κ, for all η ∈ (0, 1 2 ], there exists a constant C > 0 (depending on K, η, A 1 and A 2 ) such that for all covariance matrix S m,n , for all (1η)n j n -K log n, Var(λ j ) C log(nj) n 4/3 (nj) 2/3 .

At the edge of the spectrum.

There exists a constant C > 0 (depending on A 1 and A 2 ) such that, for all covariance matrix S m,n , Var(λ n ) Cn -4/3 .

As for Wigner matrices, the first two results of this theorem are first proved in the Gaussian case, using the fact that the eigenvalues of a LUE matrix form a determinantal process. It is then possible to derive a deviation inequality for the eigenvalue counting function and then for individual eigenvalues. Integrating leads to the results for LUE matrices. The result for the largest eigenvalue in the Gaussian case is already known, see [START_REF] Ledoux | Small deviations for β-ensembles[END_REF]. These bounds are then extended to non-Gaussian matrices, relying on three recent papers. First, Pillai and Yin proved in [START_REF] Pillai | Universality of covariance matrices[END_REF] localization properties for individual eigenvalues of covariance matrices, very similar to the localization properties for Wigner matrices established by Erdös, Yau and Yin in [START_REF] Erdös | Rigidity of eigenvalues of generalized Wigner matrices[END_REF]. Secondly, Tao and Vu (see [START_REF] Tao | Random covariance matrices: universality of local statistics of eigenvalues[END_REF]) and later Wang (see [START_REF] Wang | Random covariance matrices: universality of local statistics of eigenvalues up to the edge[END_REF]) proved a Four Moment Theorem for these matrices. Combining these theorems as for Wigner matrices yield the theorem.

Theorem 7 . 1 √N

 71 There exists a universal constant C > 0 such that the following holds. Let M N be a GUE matrix. Set W N = M N and denote by λ N the maximal eigenvalue of W N . Then, for all N ∈ N and all 0 < ε 1,

Σ 2 + Σ 4 CNext Σ 1

 41 and Σ 5 have only K log N terms. If each term is bounded by C N where C is a positive universal constant, we get that Σ 1 + Σ 5 2KC log N N , which is enough to prove the desired result on N j=1 (λ jγ j ) 2 . For N large enough depending only on constant C in Theorem 11, 1 √ N (log N ) C log log N N 2/3 min(j,N +1-j) 1/3 and Theorem 11 yields

Acknowledgements

I would like to thank my advisor, Michel Ledoux, for bringing up this problem to me, for the several discussions we had about this work, and Emmanuel Boissard for very useful conversations about Wasserstein distances.

where A > 0 is a numerical constant. Then

Repeating the same computations gives similarly

Then [START_REF] Tao | Random matrices: the distribution of the smallest singular values[END_REF] leads to

As the first two error terms are smaller than the third one, the preceding equation becomes

Combining the results

We distinguish between the bulk, the edge and the intermediate cases. Note that the constants C(η) and C depend on the constants B 1 and B 2 in condition (C0).

Eigenvalues in the bulk of the spectrum

Let 0 < η 1 2 and ηN j (1η)N. From Theorem 5, (λ ′ jγ j ) 2 C(η) log N N 2 . Thus, from [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF], it remains to show that the error term is smaller than log N N 2 . But

and we get the desired result

Eigenvalues at the edge of the spectrum

From Corollary 8, (λ

. By means of [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF], it remains to prove that the error term is smaller than N -4/3 . We have

As for Gaussian matrices, the same result is available for the smallest eigenvalue λ 1 .

Eigenvalues between the bulk and the edge of the spectrum

. Thus, from [START_REF] Tao | Random matrices: universality of local eigenvalues statistics[END_REF], it remains to show that the error term is smaller than log(N -j)

and we get the desired result

A similar result holds for the left-side of the spectrum.

Real matrices

The goal of this section is to prove Theorems 1, 2 and 3 for real Wigner matrices. Tao and Vu's Four Moment Theorem (Theorem 12) as well as Erdös, Yau and Yin's Localization Theorem (Theorem 11) still hold for real Wigner matrices. Section 2 is therefore valid for real matrices. The point is then to establish the results in the GOE case.

As announced in Section 1.2, the variance of eigenvalues at the edge of the spectrum is known to be bounded by N -4/3 for GOE matrices (see [START_REF] Ledoux | Small deviations for β-ensembles[END_REF]). The conclusion for the smallest and largest eigenvalues is then established for large families of real symmetric Wigner matrices. For eigenvalues in the bulk of the spectrum, O'Rourke proved in [START_REF] O'rourke | Gaussian fluctuations of eigenvalues in Wigner random matrices[END_REF] a Central Limit Theorem which is very similar to the one established by Gustavsson in [START_REF] Gustavsson | Gaussian fluctuations of eigenvalues in the GUE[END_REF]. In particular, the normalisation is still of the order of log N N 2 1/2 and differs from the complex case only by a constant. It is therefore natural to expect the same bound on the variance for GOE matrices. The situation is completely similar for intermediate eigenvalues. But GOE matrices do not have the same determinantal properties as GUE matrices, and it is therefore not clear that a deviation inequality (similar to ( 9)) holds for the eigenvalue counting function. However, as explained by O'Rourke in [START_REF] O'rourke | Gaussian fluctuations of eigenvalues in Wigner random matrices[END_REF], GOE and GUE matrices are linked by interlacing formulas established by Forrester and Rains (see [START_REF] Forrester | Inter-relationships between orthogonal, unitary and symplectic matrix ensembles[END_REF]). These formulas lead to the following relation between the eigenvalue counting functions in the complex and in the real cases: for all t ∈ R,

where

N and MR N are independent matrices from the GOE and ζ N (t) takes values in -1, -1 2 , 0, 1 2 , 1 . See [START_REF] O'rourke | Gaussian fluctuations of eigenvalues in Wigner random matrices[END_REF] for more details. The aim is now to establish a deviation inequality for the eigenvalue counting function similar to [START_REF] Forrester | Inter-relationships between orthogonal, unitary and symplectic matrix ensembles[END_REF]. From ( 9), we know that for all u 0,

Set C ′ 1 = C 1 + 1 and let u 0. We can then write

Repeating the computations for È N t (W R N ) -Nρ t -u -C ′ 1 and combining with the preceding yield

Note that σ 2 t is still the variance of N t (W C N ) in the preceding formula. What remains then to be proved is very similar to the complex case. From (28) and Gustavsson's bounds on the variance σ 2 t (see [START_REF] Götze | On the rate of convergence to the semicircular law[END_REF] for the bulk case and (19) for the intermediate case), deviation inequalities for individual eigenvalues can be deduced, as was done to prove Propositions 6 and 10. It is then straightforward to derive the announced bounds on the variances for GOE matrices. The argument developed in Section 2 in order to extend the GUE results to large families of Hermitian Wigner matrices can be reproduced to reach the desired bounds on the variances of eigenvalues in the bulk and between the bulk and the edge of the spectrum for families of real Wigner matrices. Then there exists a constant C(η) > 0 such that for all ηN j (1η)N,