ELECTRONIC COMMUNICATIONS in PROBABILITY
FREE GENERALIZED GAMMA CONVOLUTIONS
VICTOR PÉREZ-ABREU1
Department of Probability and Statistics, CIMAT, Apdo. Postal 402, Guanajuato, Gto. 36000, México email: pabreu@cimat.mx
NORIYOSHI SAKUMA2
Department of Mathematics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama, 223-8522, Japan
email: noriyosi@math.keio.ac.jp
SubmittedMay 14, 2008, accepted in final formSeptember 8, 2008 AMS 2000 Subject classification: 15A52, 46L54, 60E07
Keywords: free probability, infinitely divisible distribution, generalized gamma convolutions, Ran-dom matrices
Abstract
The so-called Bercovici-Pata bijection maps the set of classical infinitely divisible laws to the set of free infinitely divisible laws. The purpose of this work is to study the free infinitely divisible laws corresponding to the classical Generalized Gamma Convolutions (GGC). Characterizations of their free cumulant transforms are derived as well as free integral representations with respect to the free Gamma process. A random matrix model for free GGC is built consisting of matrix random integrals with respect to a classical matrix Gamma process. Nested subclasses of free GGC are shown to converge to the free stable class of distributions.
1
Introduction
Generalized Gamma Convolutions (GGC) is the smallest class T∗(R+)of classical infinitely divis-ible distributions onR+that contains all Gamma distributions and that is closed under classical convolution and weak convergence. This class was introduced by Thorin[16],[17]and further studied by Bondesson[7]. Thorin[18]also considered the smallest class of distributions on the real line which contains all distributions inT∗(R+)and is closed under convolution, convergence and reflection. We denote this class byT∗(R)and called it the Thorin class of distributions onR. LetP(R)be set of probability measures onRandI∗(R)the class of all classical infinitely divisible distributions in P(R). If µ ∈ P(R), µˆ(z) denotes its Fourier transform and when µ ∈ I∗(R) 1PART OF THIS WORK WAS DONE WHILE THE AUTHOR WAS VISITING KEIO UNIVERSITY DURING THE FALL OF
2007. HE ACKNOWLEDGES THE SUPPORT AND HOSPITALITY OF THE MATHEMATICS DEPARTMENT OF THIS UNIVER-SITY
2PART OF THIS WORK WAS DONE WHILE THE AUTHOR WAS VISITING CIMAT DURING THE SPRING OF 2008. HE
SINCERELY APPRECIATES THE SUPPORT AND HOSPITALITY OF CIMAT. HE IS SUPPORTED BY JAPAN SOCIETY FOR THE PROMOTION OF SCIENCE.
we denote byC∗
µ(z)its classical cumulant function or Lévy exponent i.e. Cµ∗(z) =logµˆ(z). A
probability measureµ ∈ P(R)is in I∗(R)if and only if its classical cumulant function has the Lévy-Khintchine representation :
Cµ∗(z) =−
1 2az
2+iηz+
Z
R
(e−iz x−1−iz x1{|x|≤1})ν(d x), z∈R, (1)
wherea≥0, γ∈Randν (the so called Lévy measure) is a measure satisfyingν({0}) =0 and
R
R(1∧|x|
2)<∞. The triplet(a,η,ν)is uniquely determined and is called∗-characteristic tripletor simply∗-triplet.WhenRR|x|ν(d x)<∞, we speak of thedrift type Lévy Khintchine representation
Cµ∗(z) =−
1 2az
2+iη′z+ Z
R
(e−iz x−1)ν(d x)(z∈R), (2)
whereη′is the drift ofµand is given byη′=η−R
{|x|≤1}xν(d x). We write
Ilog∗ (R) = ¨
µ∈I∗(R);
Z
R
log(|x| ∧1)µ(dx)<∞
«
and refer to Sato[13]for basic facts about classical infinitely divisible distributions.
Bondesson[7]proved that a positive random variableY with classical lawL∗(Y) =µ-without
translation term- belongs to T∗(R+)if and only if there exists a positive Radon measure Uµ on (0,∞)such that
Cµ∗(z) =− Z∞
0
(1−eiz x)dx
x
Z∞
0
e−xsUµ(ds) (3)
=− Z∞
0 log
1+iz
s
Uµ(ds)
withR1
0|logx|Uµ(dx)<∞and
R∞
1 Uµ(dx)
x <∞. The measureUµ is called the Thorin measure of µ. So, the∗-triplet ofµis(0, 0,νµ)where the Lévy measure is concentrated on(0,∞)and such that
νµ(dx) =dx
x
Z∞
0
e−xsUµ(ds). (4)
It is known that the classT∗(R+)is characterized by Wiener-Gamma representations, i.e., random integral representations with respect to the standard one-dimensional Gamma process (see[10],
[9]). Specifically, a positive random variableY belongs to T∗(R+)if and only if there is a Borel functionh:R+→R+with
Z∞
0
log(1+h(t))dt<∞, (5)
such thatY=d Yhhas the Wiener-Gamma integral representation
Yh=L Z∞
0
where γt;t≥0is the standard Gamma process with Lévy measureν(dx) =e−xdxx. Moreover,
C∗
Yh(z) =−
Z∞
0 log
1+iz
x
Uµh(dx),
whereUµhdenotes the image of Lebesgue measure on(0,∞)under the application : s→1/h(s).
That is, Z
∞
0
e−h(s)x ds=
Z∞
0
e−xzUµh(dz), x>0. (7) The functionhis calledthe Thorin functionofYand is obtained as follows. LetFUµ(x) =
Rx 0 Uµ(dy) for x≥0 and let F−1
Uµ(s)be the right continuous inverse ofF
−1
Uµ(s)in the sense of composition of
functions, that is FU−1
µ(s) = inf{t >0;FUµ(t)≥ s} for s ≥ 0. Then, h(s) = 1/F
−1
Uµ(s) fors ≥ 0.
Moreover we have the following alternative expression for the cumulant function ofµ
Cµ∗(z) =− Z∞
0 ds
Z∞
0
(1−eiz x)e −x/h(s)
x dx. (8)
In the above equation, if{t>0;FUµ(t)≥s}=φ,x/h(s) =0.
Remark 1.1. If Y is a GGC random variable, we write Yh (respectivelyµh)to indicate that it has the integral representation (6) and writeµh=L∗(Yh). We have excluded from the above discussion the case of non-zero drift, which is easily incorporated by considering nonzero drift c0in the∗-triplet
(c0, 0,νµ).
Many well known distributions belong toT∗(R+). The positiveα-stable distributions, 0< α <1, are GGC with h(s) ={sθΓ(α+1)}−α1 for aθ >0. In particular, for the 1/2−stable distribution,
h(s) =4s2π−1. First passage time distribution, Beta distribution of the second kind, lognormal and Pareto are also GGC, see[9].
As for distributions inT∗(R), there is a another random integral representation approach recently presented in Barndorff-Nielsen et. al [1], who also considered the multivariate case. We recall that if (Xt;t≥0)is a∗-Lévy process and f :[a,b]→Ris a continuous function defined on an interval [a,b] in[0,∞), then the stochastic integral R[a,b]f(t)d Xt may be defined as the limit in probability of approximating Riemann sums. Moreover, if f is continuous function defined on[0,∞),R
[a,∞)f(t)d Xt may be as the limit in probability of R
[a,b)f(t)d Xt when b→ ∞. For
stochastic integrals of nonrandom functions with respect to general additive processes we refer to Sato[14].
It is shown in[1], that for anyµinI∗(R), the mappingΥ∗given by
Υ∗(µ) =L∗ Z 1
0 log1
tdX
(µ)
t
!
, (9)
is always defined, whereXt(µ)is a Lévy process withL∗(X
(µ)
1 ) =µ. MoreoverT∗(R) = Υ∗(L∗(R)),
where L∗(R)is the class of∗-selfdecomposable distributions inR:µ∈L∗(R)if for anyb∈(0, 1)
there exists ρb ∈ P(R) such that µˆ(z) = ˆµ(bz) ˆρb(z). Furthermore, it is shown in [1] that a random variableY belongs toT∗(R)if and only if there existsµ∈Ilog∗ (R)such that
Y = Z∞
0
where the functione−11(t)is the inverse of the incomplete gamma functione1(x) =
R∞
x e
−ss−1ds andX(µ)t is a Lévy process withL∗(X(µ)
1 ) =µ.
In the study of relations between classical and free infinite divisibility, Bercovici and Pata [5]
introduced a bijectionΛbetween the setI∗(R)of classical infinitely divisible laws and the setI⊞
(R)
of free infinitely divisible laws. A new approach to this bijection was recently proposed by Benaych-Georges[4]and Cabanal-Duvillard[8]. They construct random matrix ensembles associated to classical one-dimensional infinitely divisible laws whose empirical spectral laws converge to their corresponding free infinitely divisible laws underΛ. Recall that an ensemble of random matrices is a sequence(Md)d≥1where for eachd≥1,Mdis ad×dmatrix with random entries. The (random) spectral measure (or empirical spectral law)µbMd
d of M
d is defined as the uniform distribution on
the spectrum λMd
1 , ...,λ Md
d , that is, µb Md
d = d− 1Pd
i=1δλM d i
. An ensemble (Md)d≥1 is a Random Matrix Model (RMM) for a probability measureµ, ifµbdMdconverges toµweakly in probability as d→ ∞. It is shown in[4],[8]that for anyµ∈I∗(R), there exists a random matrix model(Md)d≥1
forΛ(µ), which is constructed fromµ. These papers generalize the pioneering work by Wigner who connects Gaussian and semicircle laws throughout the Gaussian Unitary Ensemble of random matrices.
The purpose of this work is to study the free infinitely divisible laws (FGGC) corresponding to the image ofΛ of classical Generalized Gamma Convolutions and their corresponding random matrix models. We start in Section 2 by recalling facts and notation about the free cumulant function, the Bercovici-Pata bijection, free Lévy process and their random integrals. In Section 3 we prove a characterization of the free cumulant transform of a FGGC analogous to the classical cumulant transform (3). Furthermore, we derive free integral representations with respect to the free Gamma process and a Lévy process similar to (6) and (10), respectively. In Section 4 we construct random matrix models for FGGC. They are given as (classical) matrix random integrals of Wiener-Gamma type similar to (6), with respect to an appropriate (classical) matrix Gamma process. Finally, in Section 5 we point out some facts on nested subclasses ofΛ(T∗(R))and their limits, analogous to the recent results for the classical convolution case study in Maejima and Sato
[11].
2
Preliminaries on Free infinite divisibility
The Cauchy-Stieltjes transform of a probability measureµonRis defined by
Gµ(z) = Z
R
1
z−tµ(dt), z∈C
+.
The functionFµ(z) =1/Gµ(z)has right inverse Fµ−1(z)on the regionΓη,M for some M >0 and η >0, where
Γη,M:={z∈C:|Re(z)|< ηIm(z), Im(z)>M};
(see Bercovici and Voiculescu[6]). Following Barndorff-Nielsen and Thorbjørnsen[3], the free cumulant transformC⊞
µ ofµis defined by
C⊞
µ(z) =z F−
1
µ (z−
1)
−1 z−1∈Γη,M.
A probability measureµ onR is ⊞-free infinitely divisible if and only ifC⊞
µ(z)has an analytic
complete analogy to the classical case, the free Lévy-Khintchine characterization establishes that a probability measureµbelongs toI⊞
(R)if only if
C⊞
µ(z) =ηµz+aµz2+ Z
R
1
1−tz −1−tz1[−1,1](t)
νµ(dt), z∈C−, (11)
whereaµ∈R+,ηµ∈Rand the Lévy measureνµis a measure satisfyingνµ({0}) =0 andRR(|x|2∧ 1)νµ(dx)<∞. In this case, the⊞-triplet(aµ,νµ,ηµ)is uniquely determined byµand is called the
⊞-characteristic triplet or⊞-triplet forµ, see[3] [6].
Bercovici and Pata[5]introduced a bijectionΛbetween classical and free infinitely divisible dis-tributions. It is such that ifµ∈I∗(R)has∗-characteristic triplet(aµ,νµ,ηµ), thenΛ(µ)is the free
infinitely divisible distribution with⊞-triplet(aµ,νµ,ηµ).
Remark 2.1. Ifµ∈I⊞
(R)and its Lévy measureνµ satisfies R
R|x|νµ(d x)<∞, then for z∈C
−
C⊞
µ(z) =ηµz+aµz2+ Z
R
1
1−tz −1−tz1[−1,1](t)
νµ(dt)
= ηµ− Z
{|x|≤1}
xν(d x) !
z+aµz2+ Z
R
1
1−tz−1
νµ(dt)
=η′µz+aµz2+ Z
R
1 1−tz −1
νµ(dt), (12)
whereη′
µ=ηµ− R
{|x|≤1}xνµ(d x). We call this representation the drift type⊞-cumulant ofµ∈I
⊞
(R)
andη′µis the⊞-drift. By Bercovici-Pata bijection, ifµ∈I∗(R)has∗-drift type triplet(aµ,νµ,η′µ)then
the⊞-drift type triplet ofΛ(µ)is also(aµ,νµ,η′µ).
We summarize some properties of the Bercovici-Pata bijection in the following result (see[3],[5],
[6]).
Proposition 2.2. The mapΛ:I∗(R)→I⊞
(R)has the following properties. (1)Λ(µ∗ρ) = Λ(µ)⊞Λ(ρ)for anyµ,ρ∈ P(R).
(2) Letδa be Dirac measure at a. Λ(δa) =δafor a∈R. SoΛis preserved under affine transforms, i.e. Λ(Dcµ∗δa) =DcΛ(µ)⊞δafor any b>0and a∈Rwhere Dcµmeans the spectral distribution of the operator cX withµ=L(X).
(3)Λis a homeomorphism w.r.t. weak convergence i.e. µn→µif and only ifΛ(µn)→Λ(µ)in weak convergence.
For a classical random variableX or a stochastic process(Xt), we writeΛ(X)andΛ(Xt)as a short notation forΛ(L∗(X))andΛ(L∗(X
t)).
Barndorff-Nielsen and Thorbjørnsen [3]introduced free selfdecomposable distribution. A prob-ability measure µ on R is free selfdecomposable (⊞-selfdecomposable) if, for any b ∈ (0, 1), there exists ρb ∈ P(R)such thatµ= Dbµ⊞ρb. We denote by L
⊞
(R)the class of all free self-decomposable distributions onR. We refer to Sakuma[15]for a detailed study of⊞-selfdecomposable distributions.
As in the classical case, free Lévy process and their free integrals can be considered with respect to the ⊞−convolution. Given a free random variable Z, we denote byL⊞
W∗-probability space(A,τ), is a free Lévy process (in law) if it satisfies the following four condi-tions:
(1)Z0=0
(2) Whenevern∈Nand 0≤t0<t1<· · ·tn, the increments Zt0,Zt1−Zt0,Zt2−Zt1,· · ·,Ztn−Zt
n−1,
are freely independent random variables.
(3) For anys,tin[0,∞),L⊞
(Zs+t−Zs)does not depend on s. (4) For anys∈[0,∞),L⊞
(Zs+t−Zs)converge weakly toδ0, ast→0.
For any compact interval[a,b] ⊂[0,∞)and any continuous function f :[a,b]→ R, the ran-dom integralRb
a f(t)dZt exists as the limit in probability of approximating Riemann sums. The following result summarizes the connection between classical and free random integrals, see[3].
Proposition 2.3. Let(Xt)be a classical Lévy process and(Zt)be a free Lévy process with marginal distributionµt andΛ(µt), respectively. Then for any[a,b]⊂[0,∞)and any continuous function
f : [a,b] → R, the laws L∗(Rb
a f(t)dXt)and L
⊞
(Rb
a f(t)dZt) are ∗-infinitely divisible and ⊞ -infinitely divisible, respectively. Moreover,
L⊞
Z b
a
f(t)dZt
!
= Λ L∗ Z b
a
f(t)dXt
!!
. (13)
In particular, if Y is a free selfdecomposable random variable, there exists a free Lévy process Zt such thatL(Z1) =µ,
R
R\(−1,1)log(1+|t|)νµ(d t)<∞andL
⊞
(Y) =R∞
0 e− tdZ
t.
3
Free Generalized Gamma Convolutions
Whenγis the classical gamma distribution, we callΛ(γ)the free gamma distribution. If(γt;t≥0)
is the standard Gamma process, the free Lévy process(Λ(γt);t ≥0) is calledthe free standard Gamma process.
We say that a probability distribution λ is Free Generalized Gamma Convolution (FGGC) (resp. Free Thorin) if there is a classical GGC (resp. Thorin) µ such that λ = Λ(µ). We denote by T⊞
(R+) = Λ(T∗(R+))andT⊞
(R) = Λ(T∗(R))the classes of FGGC and Free Thorin class respec-tively. It follows trivially from Proposition 2.2, thatT⊞
(R+)is the smallest class that contains all free Gamma distributions and that is closed under⊞-convolution and convergence, whileT⊞
(R)
is the smallest class on the real lineR which containsT⊞
(R+)and is closed under convolution, convergence and reflection.
The following result is a characterization of the free cumulant transform of distributions inT⊞
(R+)
in terms of the Cauchy transform of the exponential distribution.
Theorem 3.1. A probability measureλinR+is FGGC without drift term if and only if there exists a Borel function h:R+→R+ satisfying (5) such thatλhas free cumulant transform
Cλ⊞(z) = Z∞
0
h(s)GE( 1
h(s))(z
−1)ds z
where GE(a)is the Cauchy transform of the exponential law with mean1/a, i.e.
Alternatively, a probability measureλinR+is FGGC without drift term if and only if there is a Thorin measure Uµhsuch that and⊞-triplet, from (12), the free cumulant transform ofΛ(γt)is obtained as
CΛ(γt)(z) =t
Next, by Remark 1.1, a probability measureλwithout drift term belongs toT⊞
(R+), if and only if
from (12) and (17), the free cumulant transform ofλis obtained as
Cλ(z) =
which proves (16) and the if part of the second statement of theorem. For the converse, letUhbe a Thorin measure andλbe a probability measure such that (16) is satisfied. Letνµh(dx)be the
Lévy measure given by (18) and letµhbe the corresponding measure inT∗(R
+). Then, from (19)
and the uniqueness of the Lévy-Khintchine representation,λhas Lévy measureνµh(dx). Thus, by
Bercovici-Para bijectionλ= Λ(µh)and thereforeλ∈T⊞
(R+).
Finally, to prove the first statement of the theorem, we use (7) in (19), proceed as in (17) and by using (15) we obtain that
Cλ(z) =
Using Propositions 2.2 and 2.3, we can easily deduce integral representations for FGGC. First, if Yh∈T∗(R+)has Wiener-Gamma representation (6), then
Λ(Yh) =L⊞
Z ∞
0
h(t)dΛ(γt)
.
Secondly, for anyµinI⊞
(R), define the mappingΥ⊞as
Υ⊞(µ) =L ⊞
Z 1
0 log1
tdZ
(µ)
t
!
.
whereZtµis free Lévy process withL⊞
(Z1(µ)) =µ. Then it is easily seen thatΛ(Υ∗(µ)) = Υ⊞(Λ(µ))
and thatT⊞
(R) = Υ⊞(L ⊞
(R)). Moreover,
T⊞
(R) = ¨
L⊞
Z ∞
0
e−11(t)dZ(µ)t
:µ∈Λ(Il o g∗ (R)) «
. (20)
We now consider some examples of FGGC. A probability measure µ on R is called free stable (⊞-stable), if the class
{ψ(µ):ψis an increasing affine transformation}
is closed under the operation⊞. LetS⊞
(R)denote the class of all free stable distributions onR. The free domains of attractions ofS⊞
(R)were studied in[5]. As in the classical case, only the free Gaussian, the Cauchy and the free 1/2−stable have densities with closed form[5]. In the next example we further study the free 1/2−stable, pointing out that it is also infinitely divisible and GGC in the classical sense.
Example 3.2. Letµ be the law of classical 12-stable law (sometimes called Lévy distribution) with scale parameter c and drift c0≥0(so its Lévy measure is ν(dr) = c r−3/2dr). It is easy to see that
Λ(µ)has density
g(x) = c
π
q
(x−c0)−c 2 4
(x−c0)2
(x> c 2
4 +c0) with Laplace transform
EΛ(µ)[exp(−r X)] = 2
πexp
−r
c2 4 +c0
Z ∞
0
(t+1)−2t12exp
−r c 2
4 t
dt r>0.
From this expression we deduce thatΛ(µ)is the Beta distribution of the second kind B2(12,32). Bondes-son[7, pp 59]proved that Beta distributions of second kind are GGC. Thus,Λ(µ)belongs to T∗(R+)
and T⊞
(R+). It is an open problem whether free stable distributions other than free Cauchy and free 1
2-stable are also infinitely divisible in the classical sense.
(1) Letµbe in T⊞
4
Random Matrix Models for Free GGC
LetMd =Md(C)denote the linear subspace of Hermitian matrices, with scalar product〈A,B〉 → tr(AB∗), forA,B ∈Md and tr denotes trace. BykMkwe denote the Euclidean norm. LetM+
d be the closed cone of nonnegative definite matrices inMd.
Let us first recall several facts on infinite divisibility of matrices taking values in the coneM+ d (see
[2]). Ad×dHermitian random matrixM is infinitely divisible inM+
d if and only if its cumulant transformCM∗(A) =logE[exp(i Tr(AM))]is of the form order of singularity Z
M+ d
min(1,kXk))ρ(dX)<∞. (22) Moreover, the Laplace transform of Mis given by
If M is an infinitely divisible matrix inM+
d, the associated matrix Lévy process{Mt}t≥0is called a matrix subordinator. It isM+
d-increasing in the sense that for all 0≤s<t,Mt−Ms∈M+d with probability one.
The matrix valued random integral
N= Z∞
0
f(t)dMt (24)
of a non-random real valued functionf is defined in the sense of integrals with respect to scattered random measures, see[12],[14]. When definable, it is ad×dinfinitely divisible random matrix with cumulant transform
C∗
N(A) =
Z∞
0 C∗
M(f(t)A)dt. (25)
Of special interest in this work is theGamma type matrix subordinatorΓ ={Γd
t}t≥0corresponding to the Lévy measure
ρdg(dX) =exp(− kXk)
kXk ωed(dX) (26) whereωed/d(E) =
R∞
0 d r
R
Sdωd(d V)1E(r V). ωd is the (probability) measure onS
+
d ={A∈M
+
d ;kAk= 1} induced by the transformationu→ V =uu∗, where the column random vector uis
uniformly distributed on the unit sphere ofCd. The Lévy measureρdghas the polar decomposition
ρdg(E) =d
Z
S+ d
ωd(dV) Z∞
0
1E(r V)e −r
r dr. (27)
We observe that ρdg has support on the subset of rank one matrices in M+
d. The case d = 1 corresponds to the Lévy measure of the one dimensional gamma process. The corresponding matrix random integralR∞
0 h(t)dΓ d
t is called thematrix Wiener-Gamma integraland is defined for Borel functionsh:R+→R+ satisfying (5).
The following is the main result of this section. It gives a RMM for FGGC on R+, where the RMM is given by matrix Wiener-Gamma type integrals, which are GGC matrix extensions of the one-dimensional case.
Theorem 4.1. Letµhbe a classical GGC onR+given by the Wiener-Gamma integral µh=L
Z ∞
0
h(t)dγt
.
The free GGCΛ(µh)has a RMM given by the ensemble of infinitely divisible matrix Wiener-Gamma
integrals
Mhd= Z∞
0
h(t)dΓdt
d≥1
, (28)
where for each d≥1,{Γd
t}t≥0is the Gamma type matrix subordinator associated to the Lévy measure ρdggiven by (26).
Proof. We shall use Theorem 6.1 in [4], which establishes that for any µ ∈ I∗(R), there is an ensemble of random matrices(Md)d≥1such that the spectral distribution ofMd converges in prob-ability toΛ(µ). Moreover, from Theorem 3.1 in[4], for eachd≥1, the Fourier transform of the random matrixMdis given by the expression
whereu= (u1, ...,ud)t is a uniformly distributed random vector on the unit sphere ofCd andCµ∗
is the cumulant function (Lévy exponent) ofµ. We will show that whenµhis a classical GGC, the random matrices(Md
h)d≥1given by (28) have the same laws as(Md)d≥1with Fourier transform (29), whereCµ∗is the cumulant transformCµ∗h ofµ
h. This will prove the theorem.
First, let µ be the one dimensional standard Gamma distribution, d ≥ 1 be fixed and u = (u1, ...,ud)t be a uniformly distributed random vector on the unit sphere of Cd. Let Γd
1 be the Gamma type matrix subordinator att=1 corresponding to the Lévy measure (26). We will show that
Then, writingV=uu∗and using the polar decomposition (27) we have
E[exp(−Tr(AΓd1))] =exp
d )d≥1be the matrix distributions of the random matrices ensemble given by (28), whereµhis a classical one dimensional GGC with Thorin functionh. Using (25), (31) and (27), we have that
From this Laplace transform and (8), we get (29).
Remark 4.2. Ifµ∈T∗(R+)and without drift, thenΛ(µ)is concentrated onR+. This follows trivially from the above construction of the RMM. As pointed out by the referee, this fact also follows from the well known equivalence
νn∗n→n→∞µ ⇐⇒ ν⊞n
n →n→∞Λ(µ).
Theorem 4.3. Letµ1be in T∗(R)given by the random integral representation
µ1=L Z ∞
0
e1−1(t)dXt(µ)
,
forµ∈Ilog∗ (R)and where X(µ)t is a Lévy process such thatL(X1(µ)) =µ.The free GGCΛ(µ1)has a RMM given by the ensemble of infinitely divisible matrix random integrals
Mhd= Z∞
0
e−11(t)dRdt
d≥1
, (32)
where for each d≥1,{Rd
t}t≥0is a matrix valued Lévy process with Lévy measureνd given by νd(E) =
Z
S+ d
ωd(dV)
Z∞
0
1E(r V)ν(dr),
withωdas in (26) andνis the Lévy measure ofµ.
5
Inheritance of nested subclasses of FGGC and its limit class
under
Λ
Maejima and Sato[11]proved that nested subclasses of classical Thorin distributions are charac-terized by limit theorem and proved that its limit class is the closure of the class of classic stable distributionsS∗(R), which is taken under ∗-convolution and weak convergence. We now point out a similar result for free Thorin distributions. The free selfdecomposable case was recently considered by Sakuma[15].
We define subclasses ofT⊞
(R)as follows. LetΨ =R∞
0 e
−1 1 (t)dZ
(µ)
t be the free integral considered in (20) andIl o g∗ m(R) ={µ∈I∗(R):
R
R(log
+
|x|)mµ(dx)<∞}. (1) Form=1, 2, ...letT⊞
m(R) = Λ(Ψ(Il o g∗ m+1(R)))andT
⊞
∞(R) =∩∞m=1T
⊞
m(R). (2)µ∈ L⊞
m(R)if, for anyc∈(0, 1), there existsρc ∈L
⊞
m−1(R)such thatµ= Dcµ⊞ρc. We also defineL⊞
∞(R) =∩∞m=0L
⊞
m(R). It was proved in[15]thatL
⊞
m(R)is⊞-c.c.s.s. and L
⊞
∞=S
⊞
(R). The following concept was introduced in the sense of classical convolution in[11].
Definition 5.1. A class M of distributions on R is said to be∗ (resp. ⊞)-completely closed in the strong sense (∗-c.c.s.s. (resp. ⊞-c.c.s.s.)), if M⊂I∗(R)(resp. M⊂I⊞
(R)) and if the following are satisfied.
(1) It is closed under∗(resp.⊞)-convolution. (2) It is closed under weak convergence.
(3) Ifµ∈M, then Dcµ∗δb∈M (resp. Dcµ⊞δb∈M) for any c>0and b∈R. (4)µ∈M implies µs∗ ∈M (resp. µs⊞
∈M) for any s>0, whereµs∗ is the distribution with the cumulant sCµ(z)(resp.µs
⊞
is the distribution with the free cumulant sC⊞
µ (z)).
The closure is taken under⊞-convolution and weak convergence.
The following result gives the preservation of classical completely closed in the strong sense class under the Bercovici-Pata bijection.
Proof. (1) and (2) in the above definition follow from Proposition 2.2. Ifµ∈Λ(M), thenΛ−1(D cµ⊞ δb) =DcΛ−1(µ)δb∈M. SoDcµ⊞δb∈Λ(M)and (3) holds. Finally, (4) holds from the classical and free Lévy Khintchine formulas.
From the above lemma and Proposition 2.3, we immediately obtain the following relationships.
Lemma 5.3. Fix0<a<∞. Suppose f is continuous on (0,a)andR0af(s)ds6=0. Let{Zt}be a free Lévy process with distributionµ. Define the mapping
Φ⊞
f(µ) =L
⊞
Z a
0
f(s)dZs(µ)
.
Then the following are true (1) If M is⊞-c.c.s.s., thenΦ⊞
f(M)⊂M. (2) If M is⊞-c.c.s.s., thenΦ⊞
f(M)is also⊞-c.c.s.s. Theorem 5.4.
T∞⊞(R) =L∞⊞(R) =S⊞
(R).
Proof. FromT⊞
m(R) = Υ m+1
⊞ (L
⊞
m(R))⊂L
⊞
m(R), we have
T∞⊞(R)⊂L⊞∞(R). (33)
Since Tm∗(R) is ∗-c.c.s.s., then Tm⊞(R) is ⊞-c.c.s.s. It is clear that Tm⊞(R) = Υm+1
⊞ (L
⊞
m(R)) ⊂
Υm⊞+1(S ⊞
(R)) =S⊞
(R). Next, sinceT⊞
m(R)is⊞-c.c.s.s.,T
⊞
m(R)⊂S
⊞
(R)and therefore,
T⊞
∞⊂S
⊞
(R) =L⊞
∞(R). (34)
Then (33) and (34) yield
T∞⊞(R) =S⊞
(R) =L∞⊞(R).
Acknowledgment.
The authors would like to thank the referee for a very careful reading of the manuscript and for the valuable suggestions and comments.
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