Tightness in Schauder decomposable Banach spaces∗

Tightness in Schauder decomposable Banach spaces ^∗

Ch. Suquet

Laboratoire de Statistique et Probabilit´es, Bˆat. M2 Universit´e des Sciences et Technologies de Lille

F-59655 Villeneuve d’Ascq Cedex France

Abstract

We characterize the tightness of a set of probability measures in a large class of Banach spaces including those having a Schauder basis. We give various applications to sequences of stochastic process viewed as random elements in the spaces L^p(0,1), L^p(IR) or in some H¨older or Besov spaces.

AMS classifications: 60B10, 60F17, 60G30.

Key words: empirical process, Haar basis, multiresolution analysis, Schauder decomposition, strong mixing, tightness, wavelets.

∗Preprint

1 Introduction

Relative compactness in the space of probability measures is a key tool in the study of weak convergence. A familyF of probability measures on the general metric space S is said to be tight if for each positiveε, there is a compact set K such thatP(K) >1−ε for all P in F. According to Prohorov’s theorem, tightness is always a sufficient condition for relative compactness and is also necessary ifS is separable and complete.

The Skorohod spaceS=D(0,1) is the usual framework of many limit the- orems for stochastic processes. This is so because it supports processes that contain jumps and weak convergence in D(0,1) provides results about some useful functionnals of paths like those involving the suprema. Nevertheless this space presents some drawbacks. First, tightness inD(0,1) is sometimes difficult to check. Second, under the pointwise addition of functions, the space D(0,1) is not a topological group and hence not a topological vector space.

In many cases, it seems very convenient to treat a stochastic process as a random element in a functional Banach space. The best known case is certainly the C(0,1) one (see Billingsley [4]). As for the Hilbert space case, sufficient conditions for tigthness are given by Prohorov [18], Parthasarathy [17] and Gihman Skorohod [8]. The recents developments in the theory of wavelets and their applications in probability and statistics show the interest of using more sophisticated functions spaces like the H¨older, Sobolev or Besov spaces.

In this paper we will present an unified approach to tightness problems in a large class of Banach spaces including C(0,1), the L^p, H¨older, Sobolev and Besov spaces. Our starting point is the Hilbertian case:

Theorem 1 (Suquet [21]) LetH be a separable Hilbert space and(e_i, i∈IN) an orthonormal basis of H. Define for each h in H and each positive integer N:

s²_N(h) = X

i<N

hh, e_ii², r_N²(h) =X

i≥N

hh, e_ii². The familyF of probability measures onH is tight if and only if:

(i) ∀N ≥1 lim

t→+∞sup

P∈F

P({h∈H : s²_N(h)> t}) = 0, (ii) ∀t >0 lim

N→+∞sup

P∈F

P({h∈H : r_N²(h)> t}) = 0.

Our aim is to generalize this theorem. Let us denote byVjthe finite dimensional subspace ofH generated by{e0, . . . , e_j−1}and byEj the orthogonal projection onVj. With these notations, the conditions (i) and (ii) can be recast as:

(i) EjF={P◦E_j⁻¹, P ∈ F }is tight for eachj≥1, (ii) ∀ε >0, lim

j→+∞sup

P∈F

P({h∈H : kh−Ejhk ≥ε}) = 0.

We would like to generalize this result in two directions: dropping the finite dimensionality of theVj(as in the case of multiresolution analysis) and dropping the orthogonality of the Ej (and hence the hilbertian character of the space).

Of course, we have to keep some control on the norms of the projectorsEj. This leads us naturally to deal with Schauder decomposable Banach spaces. The next section will present the functional needed analysis package . The third section exposes the proof of our main result: the extension of the theorem 1 for these spaces.

In the following section, we use our main theorem and a multiresolution analysis to obtain a sufficient condition for tightness in L^p(IR), 1< p <+∞.

We study also the relative compactness in L^p(0,1) of the random step func- tions involved in Donsker’s theorem (without any assumption on the dependence structure of the underlying random variables). We rederive and clarify a previ- ous condition for tightness in L²(0,1) due to Jacob, Oliveira and Suquet ([14], [15]). Next we are interested in the convergence of the empirical process based on strong mixing uniform variables (X_i)_i≥1on [0,1]. In theD(0,1) setting, the best result up to now is due to Yoshihara [23] who proved the weak convergence of the empirical process to a gaussian process under the conditionαn =O(n^−3−ε) (theαnbeing the strong mixing coefficients of the sequence (Xi)_i≥1). Recently, Oliveira and Suquet proved the same convergence inL²(0,1) under the weaker assumptionP

αn<+∞. Of course the convergence inL²(0,1) is weaker than in D(0,1). Here we obtain the convergence in L^p(0,1) (2 ≤ p < 6) under a weaker condition (depending on p) than Yoshihara’s one.

In the fifth section we are concerned with the spaces H⁰_αof H¨olderian func- tions on [0,1] (i.e. f(0) = 0, |f(t)−f(s)| ≤ C|t−s|^α and |f(t)−f(s)| = o(|t−s|^α)). We obtain a tightness criterion and a sufficient condition very similar to theC(0,1) case. Some examples are discussed.

The last section presents an easy application to some sequences spaces and their isomorphic Besov functional spaces. This application could be useful in the study of weak convergence for stochastic processes known by their wavelets coefficients.

2 The functional analysis background

We refer to Singer [20] for the Schauder decompositions and to Meyer [13] for wavelets and multiresolution analysis. Let (X, k k) be a Banach space. A system {x_n, n ∈ IN} of elements of X is called a Schauder basis if for every elementx∈ X, there is a unique series:

x=

+∞

X

n=0

a_nx_n, a_n=a_n(x)∈IR, (1) which converges to x in the norm of X. We define the associated coordinate projectionsv_n byv_n(x) =a_nx_n. These projections are continuous and there is

a constantC depending only on the basis{x_n, n∈IN} such that:

∀N ∈IN, ∀x∈ X,

N

X

n=0

vn(x)

≤Ckxk. (2)

The basis is said unconditional if the series (1) is unconditionally convergent, that is, for every permutation σ = {σ(n), n ∈ IN} of the indexes, the series P+∞

n=0a_σ(n)x_σ(n)converges toxin the norm ofX. In the multiresolution analysis setting defined below, we are dealing with bases of wavelets indexed by IN×ZZ, ZZ×ZZ, ZZ×ZZ^d. Fortunately for many functional spaces, the decomposition on these bases is an unconditionally convergent series so that neither the order of summation nor the groupings of terms do matter. Here the groupings of terms are usually made according to the level of resolution. At one given level, say 2^−j, we have a countable family of functions (ψj,k, k∈ ZZ) of the basis which is dense in a closed subspace of the involved functional space. In the important case of the Faber-Schauder basis of the space C(0,1), which do not define a multiresolution analysis, we have the groupings{∆j,k, 0≤k <2^j} which span closed finite dimensional subspaces ofC(0,1). Banach spaces having a Schauder decomposition are the natural framework unifying all these situations.

Definition 1 An infinite sequence (G_j, j ∈IN) of closed linear subspaces of a Banach spaceX such thatG_j 6={0} (j ∈IN)is called a Schauder decomposition of X if for every x ∈ X there exists an unique sequence (y_n, n ∈ IN) with yj ∈Gj (j∈IN)such that:

x=

+∞

X

j=0

yj

and if the coordinate projections defined byv_n(x) =y_n, are continuous onX. In other words, (G_j, j ∈IN) is a decomposition of X if and only if, X is the direct topological sum of the subspacesG_j. It should be noticed here that some Banach space do not possess a Schauder decomposition, for instance the space

`^∞ (see Singer [20]) and that a Schauder decomposable Banach space need not be separable.

Let us denote Vj =L

i≤jGi and Ej =P

i≤jvi the continuous projections ofX ontoVj. It follows from proposition 15.3 p. 488 in Singer [20] that:

C= sup

j∈IN

kEjk<+∞. (3)

By the orthogonality relations between the coordinate projections (i.e.: vi◦vj= δ_ijv_i=δ_ijv_j) we have:

E_j⁰◦E_j=E_j◦E_j⁰ =E_j∧j⁰, j, j⁰ ∈IN. (4)

This implies:

kx−Eixk ≤(1 +C)kx−Ejxk, x∈ X, i > j. (5) In separable Banach spaces having a Schauder decomposition, we have a very simple criterion for relative compactness. This criterion is a generalization of the Hilbert space case (see for instance Akhiezer and Glazman [1]). We did not find this result in its general form in the literature, so we give a detailed proof.

Theorem 2 Let X be a separable Banach space having a Schauder decomposi- tion. A subset K is relatively compact in X if and only if:

(i) For eachj∈IN,EjK is relatively compact in Vj, (ii) sup

x∈K

kx−E_jxk →0 asj→+∞.

Proof :

Sufficiency of (i) and (ii): Let (z_n, n ∈ IN) be a sequence in K. We have to check that (z_n, n∈IN) contains a convergent subsequence (z_n, n∈I) where I is some infinite subset of IN. Using repeatedly (i), we can construct a sequence (J_j) of infinite subsets of IN such that: IN⊃J₀⊃J₁⊃ · · ·and for eachj, (E_jz_n, n ∈Jj) converges inVj. Moreover we can require that min(Jj)<min(Jj+1), j ∈ IN. Let us define then I = {min(Jj), j ∈ IN}. By construction we have {n ∈ I, n ≥ min(Jj)} ⊂ Jj for each j ∈ IN and hence the sequence (Ejzn, n ∈ I) converges in Vj towards some yj. Now we show that (zn, n ∈ I) is a Cauchy sequence in X. For fixed positive ε there is by (ii) an integer j such thatkx−Ejxk< εfor allxinK. By the construction ofI, there is an integer n0 such that for alln > n0 in I, kEjzn−yjk < ε. So for every nandplarger thann0 inI,kzn−zpk<4ε.

Necessity of (i) and (ii): The necessity of (i) follows obviously from the con- tinuity of the projections Ej. To prove (ii), we can assume without lose gen- erality that K is closed and hence compact. Thus the continuous function x7→ kx−Ejxk takes its maximum overK for somezj ∈K. Put:

y_j =z_j−E_jz_j, ky_jk = sup

x∈K

kx−E_jxk.

It suffices then to prove the convergence to zero of (y_j, j∈IN). First we observe that (y_j, j ∈ IN) is relatively compact in X. Indeed, taking subsequences it suffices to check that if (z_j, j∈J) converges toz, (E_jz_j, j∈J) is a convergent sequence. Writing:

kEjzj−zk ≤ kEjk · kzj−zk+kEjz−zk,

this follows from (3) and the definition 1. Moreover we have limE_jz_j = limz_j, so that the only possible limit for a subsequence of (y_j j ∈IN) is zero. Hence y_j converges to 0, which ends the proof.

Let us now have a more detailed look at the examples of Schauder decom- positions referred above. The first instance is provided by the Schauder bases (xn, n ∈ IN), taking Gj = span[xj]. For some bases it is more convenient to have the Gj as span of a finite number of vectors of the basis. This is the case of the Haar and Faber-Schauder bases we are now recalling the definition.

The Haar basis (en, n∈IN) is an unconditional basis for the spaces L^p(0,1), (1< p <+∞). Put ψ(t) = 1I_[0,1/2[(t)−1I_[1/2,1[(t). The Haar basis is defined by e0(t) = 1 and en(t) = ej,k(t) = 2^j/2ψ 2^jt−k

where n = 2^j +k with 0 ≤k <2^j. Here the Schauder decomposition of L^p(0,1) we are interested in is given by G0 = span[e0], Gj = span[ej,k, 0≤k <2^j]. Let us recall that the projection of f onto Vj =L

0≤i≤jGi is its approximation by a step function equal to the mean value off over each intervall

k2^−j,(k+ 1)2^−j

, 0≤k <2^j. The Faber-Schauder basis (∆_n, n∈IN∪ {−1}) is a Schauder basis for the spaceC(0,1) of continuous functions on [0,1]. This space have no unconditional basis. Put ∆(t) = 2t1I_[0,1/2[(t) + 2(1−t)1I_[1/2,1[(t). Then ∆₋₁(t) = 1, ∆₀(t) =t,

∆1(t) = ∆0,0(t) = ∆(t), ∆n(t) = ∆j,k(t) = ∆(2^jt−k) where n= 2^j+k with 0 ≤ k < 2^j. Here we define the Gj as in the Haar basis case (and G₋₁ = span[∆₋₁]). The projection of a continuous function f onto Vj is simply its approximation by linear interpolation with knots k2^−j, f(k2^−j)

. The Faber Schauder basis is also a Schauder basis in the H¨olderian spaces H⁰_α(see section 5 below).

Finally, we recall some useful facts about wavelets and multiresolution anal- ysis (in a reduced version adapted to our purpose, the general definitions can be found in Meyer [13] or Daubechies [7]). In what follows, for g∈L²(IR), we write gj,k for the functiongj,k(t) = 2^j/2g 2^jt−k

, j, k ∈ZZ. By multiresolu- tion analysis with scaling functionϕ, we mean a ladder of closed subspaces (V_j, j∈ZZ) of L²(IR) such that:

a) \

j∈ZZ

Vj={0}, [

j∈ZZ

Vj =L²(IR), b) Vj ⊂Vj+1,

c) Vj = span [ϕj,k, k∈ZZ],

d) (ϕ_0,k, k∈ZZ) is an orthonormal basis ofV₀.

The multiresolution analysis is called r-regular (r∈IN) ifϕis ofC^r class and for each integermthere is a constantamsuch that:

ϕ^(α)

(t)≤am 1 +|t|^−m

, t∈IR, α≤r. (6)

Define W_j as the orthogonal complement ofV_j in V_j+1, then we have for each j∈ZZ the decomposition:

L²(IR) =Vj⊕M

i≥j

Wi. (7)

One can construct a functionψsuch that (ψ_0,k, k∈ZZ) is an orthonormal basis of W0 and (ψj,k, j, k ∈ ZZ) is an orthonormal wavelets basis of L²(IR). If the multiresolution analysis is r-regular, ψ verify also the property (6). We write Ej (resp. Dj) for the orthogonal projection fromL²(IR) ontoVj(resp. Wj) and its associated integral kernel:

Ejf = Z

IR

Ej(. , s)f(s)ds, Ej(t, s) =X

k∈ZZ

ϕj,k(t) ¯ϕj,k(s), (8)

Djf = Z

IR

Dj(. , s)f(s)ds, Dj(t, s) =X

k∈ZZ

ψj,k(t) ¯ψj,k(s). (9) The kernelsEj andDj verify:

Ej(s, t) = 2^jE0(2^js,2^jt), Dj(s, t) = 2^jD0(2^js,2^jt), s, t∈IR. (10) Using (6), it is easily verified that E0 and D0 are majorized by convolution kernels. More precisely, there exists two rapidly decreasing functionsK and L such that:

E₀(s, t)

≤K(s−t),

D₀(s, t)

≤L(s−t). (11) As shown in Meyer [13], the usefulness of the wavelets bases associated to a regular multiresolution analysis goes far beyond theL²(IR) space. They provide unconditional bases for many functions spaces as L^p(IR) (1< p <+∞), Sobolev, H¨older and Besov spaces. In each case, the function space X is the topological direct sum of V0 and the Wi (i ≥ 0) (these subspaces being redefined in an adapted way). We have then a Schauder decomposition ofX given byG0=V0, Gj=W_j−1 (j≥1).

3 Main result

We give now the characterization of the tightness for separable Schauder de- composable Banach spaces.

Theorem 3 Let X be a separable Banach space having a Schauder decomposi- tion:

X =

+∞

M

i=0

Gi, Vj=

j

M

i=0

Gi, j= 0,1,2, . . .

and denote byEj the continuous projection fromX ontoVj. LetF be a family of probability measures onX andEjF={µ◦E_j⁻¹, µ∈ F }. ThenF is tight if and only if:

(i) EjF is tight,j = 0,1,2, . . . (ii) For each positiveε, lim

j→+∞sup

µ∈F

µ x∈ X : kx−E_jxk > ε

= 0.

Proof :

Sufficiency of (i) and(ii): For fixed positive η, put ηl = 2^−l, l = 1,2, . . . and choose a sequence (εl) decreasing to 0. By (ii), there is an integerjl such that:

∀µ∈ F, µ x∈ X : kx−Ej_lxk > εl

< ηl. (12) By (i), there is a compact subsetKlofX such that:

∀µ∈ F, µ x∈ X : Ej_lx∈Kl

>1−ηl. (13) From (12) and (13) we deduce:

∀µ∈ F, µ \

l≥1

x∈ X : Ej_lx∈Klandkx−Ej_lxk ≤εl

>1−2η. (14) It remains to check the compacity inX of the intersection in (14). This follows easily from the continuity of theEj, (4), (5) and the theorem 2.

Necessity of(i)and(ii): As tightness is preserved by continuous mappings, the necessity of (i) follows from the continuity of theE_j. To prove the necessity of (ii), we need the following lemma.

Lemma 1 Let F be a compact family (for the topology of weak convergence) of probability measures on the separable metric space S. Let (F_l, l ∈IN) be a sequence of closed subsets ofS decreasing to∅. Define the functionsu_l (l∈IN) by: u_l: P 7−→u_l(P) =P(F_l). Then the sequence(u_l)uniformly converges to zero onF.

Proof : For positive ε, let us define Dl,ε ={P ∈ F : ul(P) ≥ε}. We first verify that Dl,ε is closed. The topology of weak convergence on probability measures over S being metrizable, this can be done by means of sequences.

Let (Pn, n∈IN) be a sequence inDl,ε, weakly convergent to some P. By the portmanteau theorem we have:

u_l(P) =P(F_l)≥lim sup

n→+∞

P_n(F_l) = lim sup

n→+∞

u_l(P_n)≥ε,

so P is in Dl,ε, which is then closed. The monotone continuity of probability measures implies clearly T

l∈INDl,ε =∅. In view of the compactness of F, we can find an l0 such that T

l≤l0Dl,ε =∅. As the sequence (Dl,ε) decreases, we have: u_l(P)< εfor alll≥l₀,and allP ∈ F.

Now we apply this lemma withFj taken as the closure of:

Aj ={x∈ X, sup

i≥j

kx−Eixk ≥ε}.

Clearly (Aj, j ∈IN) is decreasing and so is (Fj, j∈IN). Using (5), we have:

∀x∈Aj, kx−Ejxk ≥ ε 1 +C.

By continuity ofId−E_j, this remains true for all xinF_j. As for each xin X, lim_j→+∞Ejx=x, this implyT

j∈INFj=∅. Sinceµ(x∈ X, kx−Ejxk ≥ε)≤ µ(Fj), the lemma 1 give us the expected conclusion.

When the subspaces of the Schauder decomposition are finite dimensional, the theorem 3 has the following more tractable version:

Theorem 4 Assume the Banach spaceX has the Schauder decompositionX = L

j∈ING_j where each G_j is of finite dimension. Then F is tight if and only if the condition(ii) of theorem 3 and the following condition(i⁰)hold:

(i⁰) lim

A→+∞sup

µ∈F

µ x∈ X : kxk> A

= 0.

Proof : Clearly (i⁰) holds ifFis tight. On the other hand,kxk and sup_jkEjxk being equivalent norms inX (Singer [20], prop. 15.3 b) p. 488), (i⁰) implies:

A→+∞lim sup

µ∈F

µ x∈ X : kE_jxk > A

= 0, j = 0,1,2, . . . As theV_j are finite dimensional, the tightness ofE_jF follows.

The theorem 4 applies in particular to the decompositions coming from a Schauder basis. As a result, combining theorems 3 and 4, we can treat the case of multiresolution analysis. Indeed, by an elementary topological argument, tightness inVj reduces to tightness inV0 and in theWi (0≤i < j). And these spaces have a wavelets Schauder basis.

By Markov’s inequality, the sufficient conditions for tightness in theorem 4 admit the following moment form.

Theorem 5 Assume the Banach spaceX has the Schauder decompositionX = L

j∈ING_j where each G_j is of finite dimension. ThenF is tight if:

(i) ∃α >0, sup

µ∈F

IE_µkξk^α<+∞, (ii) ∃β >0, lim

j→+∞sup

µ∈F

IEµkξ−Ejξk^β = 0,

whereξ denotes a random element in X with distribution µandIEµ the expec- tation with respect to µ.

This last theorem is a generalization of the theorem 1.13 of Prohorov [18] for the Hilbertian case. According to this theorem (see also Parthasarathy [17] th.

2.2 p. 154), whenX is a separable Hilbert space,F is tight if:

j→+∞lim sup

µ∈F

Z

X

r_j²(x)µ(dx) = 0, (15)

wherer²_j(x) =P

i≥jhx, eii² and (e_i, i∈IN) is an orthonormal basis ofX. That is exactly the condition (ii) above withβ = 2 for the Schauder decomposition associated to the basis (e_i, i∈IN).

In [21] the Prohorov-Parthasarathy’s statement was shown to be incomplete according to the following counter example: take F as the canonical image in Vd = span[e0, e1, . . . , ed] of a non-tight family in IR^d. Clearly (15) is satisfied butFis not tight. The author proposed a rectified statement by adding to (15) the condition (i) of theorem 5 with α = 2. So the theorem 5 is effectively a generalization of the rectified Prohorov’s theorem.

Let us consider a sequence of stochastic processes as a sequence of random elements in X. The conditions (i) or (i⁰) of the theorems above are related to the size of the paths (in the norm of X). As it will be illustrated in the following sections, in many cases, the conditions (ii) involve the oscillations of the processes (in a sense depending upon the norm ofX). Our next result roughly says that, if the sample path’s size is well controlled, there is no real loss in investigating the tightness of the sequence by mean of the moment condition (ii) of theorem 5.

Theorem 6 Suppose condition(i)of theorem 5 holds. Then condition(ii)with β < αis necessary for the tightness of F.

Without loss of generality, we can supposeF compact. Then condition (ii) will result from the following stronger one:

j→+∞lim sup

µ∈F

IEµ

sup

i≥j

kξ−Eiξk^β

= 0. (16)

The first step is to prove the continuity of the functionals:

T_j: µ7−→IE_µ sup

i≥j

kξ−E_iξk^β

, j∈IN.

To this end, consider a sequence (µn) weakly converging to the probability measure µand the random elements ξn, ξwith respective distributions µn, µ.

We introduce the non negative random variables:

X_n = sup

i≥j

kξ_n−E_iξ_nk^β, X= sup

i≥j

kξ−E_iξk^β. Puttingα/β= 1 +ε, we have by (3) a constantAsuch that:

IE_µ X_n^1+ε

≤AIE_µkξ_nk^α, n∈IN.

So (i) implies the uniform integrability of the sequence (Xn, n∈IN). Now we observe that the functionfj :x7→sup_i≥jkx−Eixk^β is continuous onX. This easily follows from (3) and (5). Thus, as ξn converges toξin distribution, the same is true forXn=fj(ξn) andX=fj(ξ). So IEXn converges to IEX, which proves the continuity ofT_j.

Now (T_j, j ∈ IN) is a decreasing sequence of continuous functions on the compactF. According to Dini’s theorem, (T_j, j ∈IN) converges uniformly on

F. To identify the limit, observe that:

j→+∞lim Tj(µ) = lim

j→+∞

Z

X

sup

i≥j

kx−Eixk^βdµ(x) = 0, by (3), (i) and the dominated convergence theorem.

Before closing this section, we test our tightness criterion in the well known case of the spaceC(0,1). Classically, tightness in this space is characterized via the Arzela-Ascoli theorem by ([4], th. 8.2):

(a) For each positiveη, there is ana such that µ x∈C(0,1) : |x(0)|> a

≤η, ∀µ∈ F (b) For each positive ε, lim

δ→0sup

µ∈F

µ x ∈ C(0,1) : w(δ, x) ≥ ε

= 0, where w(δ, x)is the modulus of continuity.

This criterion can be rederived as a corollary of theorem 4:

Proposition 1 For the Schauder decomposition associated to the Faber-Schau- der basis of C(0,1), the conditions (i⁰) and (ii) of theorem 4 are equivalent to the conditions(a)and(b).

Proof : Recalling that for the Faber-Schauder basis, Ejxis the linear inter- polation of x with knots k2^−j, x(k2^−j)

, the following inequalities are easily checked:

kx−Ejxk∞≤w(2^−j, x), x∈C(0,1), (17) w(δ, x)≤2kx−Ejxk∞+ 2kxk∞2^jδ, 0< δ <2^−j, x∈C(0,1). (18) Now, using (18), it follows that (i⁰) and (ii) imply (b). Moreover (i⁰) implies obviously (a). On the other hand, by (17), (b) implies (ii). Finally, since

|x(t)| ≤ |x(0)|+¹_δw(δ, x), (i⁰) follows from (a) and (b).

4 Some applications in L

spaces

From now, we specialize in the case of stochastic processes considered as random elements in functions spaces. By definition a sequence of such processes is tight when the sequence of corresponding distributions is tight.

4.1 A sufficient condition in L

(IR)

We shall use a r-regular multiresolution analysis of L^p(IR) to obtain a suffi- cient condition for tightness of stochastic processes sequences in L^p(IR). Let us first recall how the multiresolution analysis works in L^p(IR). From a r-regular multiresolution analysis (Vj, Wj; j ∈ZZ) of L²(IR), we define the spacesVj(p), Wj(p) in the following way (Meyer [13] p. 31 and 45):

Case1< p <2: V₀(p) =V₀∩L^p(IR),W_j(p) =W_j∩L^p(IR),

Case2< p <+∞: V₀(p) andW_j(p) are the completions ofV₀ andW_j in theL^p norm.

In both cases, Vj(p) is defined by change of scale: f(t)∈ V0(p) if and only if f(2^jt)∈Vj(p). Moreover we haveVj+1(p) =Vj(p)⊕Wj(p) and

L^p(IR) =V₀(p)⊕

+∞

M

j=0

W_j(p).

All these sums are direct and topological (i.e. the projections on the components are continuous in the L^p norm), so we have a Schauder decomposition defined by: G0=V0(p),Gj=W_j−1(p).

Theorem 7 A sequence(ξn, n∈IN)of stochastic processes with paths inL^p(IR) (1< p <+∞) is tight inL^p(IR) if:

(i) sup

n∈IN

Z

IR

IE|ξn(t)|^pdt <+∞, (ii) lim

A→+∞sup

n∈IN

Z

{|t|≥A}

IE|ξn(t)|^pdt= 0,

(iii) There is aγ >0 and a function g∈L¹(IR)such that:

sup

n∈IN

IE|ξn(t+u)−ξn(t)|^p≤ |u|^γg(t), u, t∈IR.

Proof : The condition (ii) of theorem 3 will follow from the sufficient moment condition:

j→+∞lim sup

n∈IN

IEkξn−Ejξnk^p_p= 0 (19) An easy adaptation of the proof of theorem 9.1.6. in Daubechies [7] shows that if ϕ is C¹ and |ϕ(t)|, |ϕ⁰(t)| ≤ a(1 +|t|)^−1−ε, then {ϕj,k, k ∈ ZZ} is an unconditional basis of V_j(p). So we can use the theorem 5 to investigate the tightness of (E_jξ_n, n∈IN) inV_j(p). From now, let us choose the scaling function ϕreal, compactly supported andC¹.

Applying theorem 5 withα=β=p, we have to check:

sup

n∈IN

IEkEjξnk^p_p<+∞ (20) and

l→+∞lim sup

n∈IN

IEkEjξn−Ej,lξnk^p_p = 0, (21) where E_j,l is the projection from V_j(p) onto the finite dimensional subspace span[ϕj,k, |k| ≤l].

By an elementary density argument, the integral representation forEj re- mains true in L^p(IR):

E_jf(t) = Z

IR

E_j(s, t)f(s)ds, f ∈L^p(IR). (22) WriteKj(u) for 2^jK(2^ju) whereKis the majorizing convolution kernel involved by (11). We have for eachf ∈L^p(IR):

kE_jfk^p_p≤ Z

IR

Z

IR

K_j(s−t)|f(s)|ds

p

dt=kK_j∗ |f| k^p_p≤ kK_jk^p₁kfk^p_p and hence (20) follows from the hypothesis (i).

Since eachϕj,k(k∈ZZ) has a compact support localized aroundk2^−j, there is a sequence (A_l)_l∈INincreasing to +∞such that:

E_j(s, t)−E_j,l(s, t) = 0, |s| ≤A_l, t∈IR.

As a result, we have for eachf ∈L^p(IR) : IEkEjf−Ej,lfk^p_p =

Z

IR

Z

IR

Ej(s, t)−Ej,l(s, t) f(s)ds

p

dt

≤ Z

IR

Z

IR

Kj(s−t)|f(s)|1I_{|s|>Al}(s)ds

p

dt

≤ kKjk^p₁ Z

{|s|>Al}

|f(s)|^pds.

So (21) follows from the hypothesis (ii).

It remains to verify the condition (19). For this part of the proof, the compacity of the support of ϕ is not needed. It follows from the proof of theorem 4 p. 33 in Meyer [13] that:

Z

IR

Ej(s, t)dt= 1, s∈IR.

Consequently, for eachf ∈L^p(IR), we have:

kf −Ejfk^p_p = Z

IR

Z

IR

Ej(s, t) f(s)−f(t) dt

p

ds

≤ Z

IR

bj(s)^p Z

IR

|Ej(s, t)|

bj(s) |f(s)−f(t)|dt ^p

ds,

wherebj(s) =R

IR|Ej(s, t)|dt. By Jensen’s inequality it follows:

kf−Ejfk^p_p≤ Z

IR²

bj(s)^p−1|Ej(s, t)||f(s)−f(t)|^pdsdt.

We now check that b_j(s) is bounded uniformly inj and s. Recalling that for each integermthere is a constantamsuch that: ϕ(t)≤am(1 +|t|)^−m, we get:

bj(s) = Z

IR

|E0(2^js, t)|dt≤a²_m Z

IR

dt (1 +|t|)^m

X

k∈ZZ

1

(1 +|2^js−k|)^m. (23) Let us write θj(s) for the series in (23). Clearly θj(s) = θ0(2^js) and hence kθjk∞ =kθ0k∞. Now, θ0 is periodic with period 1 and continuous (assuming m≥2) sokθ0k∞<+∞.

Going back to the stochastic processesξn, we have then a constantB such that:

IEkξn−E_jξ_nk^p_p≤B Z

IR²

|Ej(s, t)|IE|ξn(s)−ξ_n(t)|^pdsdt.

By the properties of the majorizing kernel K, there is for each integer m a constantcmsuch that:

|Ej(s, t)| ≤ cm2^j

(1 + 2^j|s−t|)^m, (s, t)∈IR². The hypothesis (iii) yields now a constantBmsuch that:

IEkξn−Ejξnk^p_p≤Bm

Z

IR

g(t)dt Z

IR

2^j|u|^γ (1 + 2^j|u|)^mdu.

Choosingm≥γ+ 2, we have by the change of variablev= 2^ju:

IEkξn−Ejξnk^p_p≤Bmkgk12^−jγ Z

IR

|v|^γ (1 +|v|)^mdv, so (19) is satisfied and the proof is complete.

4.2 Donsker random step functions

Let (Xi)_i≥1 be a sequence of centered random variables. Here the Xi need not have any special properties as independence or mixing. Write S0 = 0, Sn = Pn

i=1Xi and consider the Donsker random step functions ξn and the random broken linesζn defined by:

ξn(t) =

n−1

X

i=0

Si

s_n1II_n,i(t), whereIn,i= i

n,i+ 1 n

, t∈[0,1], (24) ζ_n(t) = S_[nt]

sn

+ nt−[nt]X_[nt]+1 sn

, t∈[0,1]. (25)

Here the normalizing sequence (sn) is of the form:

sn =n^δL(n), 0< δ <1,

where L is a slowly varying function verifying infx≥1L(x)> 0. The study of the tightness of (ξn)n≥1 in D(0,1) and of (ζn)n≥1 in C(0,1) has been carried out to prove invariance principles. Here the tightness of (ξn)n≥1 in L^p(0,1) (1< p <+∞) is considered in itself, as an indicator of the more or less chaotic asymptotic behavior of (ξ_n)_n≥1.

Theorem 8 The sequence (ξn)n≥1of random functions defined by (24) is tight inL^p(0,1)(1< p <+∞) if there is a constant Asuch that:

IE

S_k+m−S_k sm

p

≤A, m∈IN^∗, k∈IN. (26) Corollary 1 If the sequence(X_i)_i≥1 is stationary and verify:

IE

S_m sm

p

≤A, m∈IN^∗, (27)

then(ξ_n)_n≥1 is tight in L^p(0,1) (1< p <+∞).

Remark: Consider the linear injectionT fromC(0,1) into L^p(0,1):

T : f 7−→

n−1

X

i=0

fi n

1II_n,i. We have:

kT fkp= 1

n

n−1

X

i=0

fi n

p^1/p

≤ kfk_∞,

so T is continuous. Hence tightness of (ζ_n)_n≥1 in C(0,1) implies tightness of (ξ_n)_n≥1 in L^p(0,1). This bounds the field of interest of our result in the

stationary case. Indeed, it easily follows from theorem 12.2 p. 94 in [4], that if (27) holds withp >2 andsm=√

mthen:

P max

1≤i≤m|Si| ≥λ√ m

≤ C λ^p, and so (ζ_n)_n≥1is tight inC(0,1).

Proof : Using the Schauder decomposition associated to the Haar basis (see section 2), we will verify the conditions (i) and (ii) of theorem 5 withα=β=p.

Observe first that:

IEkξnk^p_p= IE Z 1

0

|ξn(t)|^pdt= 1 n

n−1

X

i=0

IE

Si

sn

p

≤A, so (i) is satisfied.

Before treating the condition (ii), it seems convenient to recall here some useful facts about theEj associated to the Haar basis. The projection operator Ej has an integral kernel: Ejf(s) =R1

0 Ej(s, t)f(t)dt,f ∈L^p(0,1). It is easily verified that:

Ej(s, t) = 2^j

2^j−1

X

k=0

1IC_j,k(s, t), whereCj,k= k

2^j,k+ 1 2^j

²

, (s, t)∈[0,1]². In other words, (s, t) 7→ Ej(s, t) is the uniform density over its support, the unionC_j of the diagonal squares C_j,k (0≤k <2^j). It follows thatt7→E_j(s, t) is the uniform density over the segment [k2^−j,(k+ 1)2^−j[ which containss. As R1

0 Ej(s, t)dt= 1, we have the following representation:

kξn−Ejξnk^p_p= Z 1

0

Z 1

0

Ej(s, t)

n−1

X

i=0

Si

sn

1II_n,i(s)−1II_n,i(t) dt

p

ds. (28) We will find an upper bound for kξn −Ejξnk^p_p in the two cases n > 2^j and n <2^j. Ifn= 2^j, by the mean value interpretation ofEj, we haveξn=Ejξn.

The casen >2^j: Recasting (28) as:

kξn−Ejξnk^p_p=

n−1

X

i=0

Z ⁱ⁺¹_n

i n

n−1

X

l=0

Z ^l+1_n

l n

Ej(s, t)Si−Sl

s_n

p

ds,

we get by convexity:

kξn−E_jξ_nk^p_p ≤

n−1

X

i,l=0

S_i−S_l sn

pZ ⁱ⁺¹_n

i n

Z ^l+1_n

l n

E_j(s, t)dsdt (29)

≤ 2^j n²

n−1

X

i,l=0

Si−Sl

sn

p

a(n, i, l), (30)

where a(n, i, l) = 1 if the double integral in (29) is positive,a(n, i, l) = 0 else.

So the sum in (30) is composed of 2^j square blocks:

n−1

X

i,l=0

Si−Sl

sn

p

a(n, i, l) =

2^j−1

X

k=0

X

(i,l)∈Qj,k,n

Si−Sl

sn

p

,

where the square index setsQj,k,n=n

(i, l) : k 2^j −1

n < i n, l

n < k+ 1 2^j

o ,have side lenghtqj,k,nbounded by: n2^−j≤qj,k,n≤n2^−j+ 2. Renormalizing|Si−Sl| by somem^δL(m), withm=qj,k,n, we obtain:

kξn−Ejξnk^p_p≤ 2^j n²

2^−j+ 2 n

pδ2^j−1

X

k=0

L(m) L(n)

p X

(i,l)∈Qj,k,n

Si−Sl

m^δL(m)

p

.

By the properties of slowly varying functions, we have L(x)/L(y)≤a(y/x)^δ/2 (1≤x≤y) for some constanta. Using the hypothesis (26), we have then some constantA⁰ such that:

IEkξn−Ejξnk^p_p≤A⁰2^−jpδ/2, (n >2^j). (31) The casen <2^j: In view of the diagonal localization of the supportC_j, (28) can be recast as:

kξ_n−E_jξ_nk^p_p=

2^j−1

X

k=0

Z ^k+1_2j

k 2j

Z ^k+1_2j

k 2j

2^j

n−1

X

i=0

S_i sn

1I_In,i(s)−1I_In,i(t) dt

p

ds.

IfC_j,k is included in some I_n,i² , the corresponding term in the summation over k above vanishes. Hence we have only to consider the ”small” squares Cj,k,i

which cover the junction of two consecutives ”big” squares,I_n,i−1² andI_n,i² .

i−1 n i n i+1

n

i−1 n

i n

i+1 n

Cj,k,i

So we have:

kξn−Ejξnk^p_p =

n−1

X

i=1

Z ^ki+1

2j ki 2j

Z ^ki+1

2j ki 2j

2^jh

1IIn,i−1(s)−1IIn,i−1(t)Si−1

sn

+ 1II_n,i(s)−1II_n,i(t)Si

s_n i

ds

p

dt (32)

≤ 2^j

n−1

X

i=1

Z

C_j,k,i

1I_In,i−1(s)−1I_In,i−1(t)S_i−1 sn

+ 1II_n,i(s)−1II_n,i(t)Si

s_n

p

dsdt, (33) by the Jensen inequality. As the function under the integral in (33) vanishes on the traces of I_n,i−1² and I_n,i² over C_j,k,i, the integral in (33) is bounded by s^−p_n |S_i−S_i−1|^ptimes the area of C_j,k,i. Hence:

kξn−Ejξnk^p_p≤ 1 2^j

n−1

X

i=1

Si−S_i−1 s_n

p

, n <2^j. Taking the expectations, we get for some constantA⁰⁰:

IEkξn−Ejξnk^p_p≤ A⁰⁰ 2^jn^pδ−1,

which provides the following uniform bounds in the casen <2^j: IEkξn−Ejξnk^p_p≤

A⁰⁰2^−j if p≥1/δ

A⁰⁰2^−jpδ if 1< p <1/δ. (34) Finally, condition (ii) of theorem 5 follows from (31) and (34).

4.3 Donsker random step functions in L

(0, 1)

The following theorem about the particular case p= 2 was first published by Oliveira [14]. The proof used the Prohorov’s theorem 1.13 [18], the reproduc- ing spaceHK with kernelK(s, t) = 1−max(s, t) and some trigonometric basis of HK. Oliveira and Suquet [15] presented a new version of the proof taking account of the rectification in Prohorov’s theorem and working with any hilber- tian basis ofHK. Now we are giving a third proof of the same result, in a more natural way without use of the reproducing kernel Hilbert spaces.

Theorem 9 The sequence (ξn)n≥1 defined by (24) is tight inL²(0,1) if there is a constant B such that:

1 s²_n

n

X

i,l=1

IEXiXl

≤B, n≥1. (35)

Proof : We work again with the Haar basis. We use the representation ofξ_n involving directly the random variablesXi:

ξ_n(t) =

n

X

i=1

X_i sn

1I_Jn,i(t), whereJ_n,i= i

n,1

, t∈[0,1].

Puth_n,i(s, t) = 1I_Jn,i(s)−1I_Jn,i(t). By the properties ofE_j(s, t) recalled in the proof of theorem 8, we have:

kξn−Ejξnk²₂ = Z 1

0

n

X

i=1

Xi

sn

Z 1

0

Ej(u, t)hn,i(u, t)dt

2

du

=

n

X

i,l=1

X_iX_l s²_n

Z 1

0

du Z 1

0

Z 1

0

E_j(u, s)E_j(u, t)h_n,i(u, s)h_n,l(u, t)dsdt

= 1

s²_n

n

X

i,l=1

XiXlb(n, i, l),

where:

b(n, i, l) =

2^j−1

X

k=0

2^2j Z

[^k

2j,^k+1

2j [³

h_n,i(u, s)h_n,l(u, t)dsdtdu. (36) Observe that if ⁱ⁺¹_n ≤₂^kj or _nⁱ ≥ ^k+1₂j , h_n,i(u, s) vanishes on [₂^k_j,^k+1_2j [² and else

|hn,i| is bounded by 1. We see so there is at most one non null term in the right hand side of (36) under the constraints ₂^k_j ≤_nⁱ ≤ ^k+1_2j and ₂^k_j ≤_n^l ≤ ^k+1_2j . Hence we always have 0≤b(n, i, l)≤2^−j. Taking expectations, we get:

IEkξn−E_jξ_nk²₂= 1 s²_n

n

X

i,l=1

IEX_iX_lb(n, i, l)≤B2^−j. So condition (ii) of theorem (5) is satisfied.

To check the condition (i), an elementary calculation yields:

IEkξnk²₂= 1 s²_n

n

X

i,l=1

IE(XiXl)

1−max(i, l) n

≤B, n≥1.

If the sequence (Xi)i≥1is independent or stationary or positively dependent, then (35) reduces tos⁻²_n IES_n² ≤B. But in the general case the absolute value cannot be dropped in (35) without extra assumption on the (Xi)i≥1. We give now an example (with sn=n^1/2) of sequence (Xi)i≥1 verifyingn⁻¹IES²_n≤B but with (ξ_n)_n≥1not tight in L²(0,1) .

LetX₁be a random variable following the uniform distribution on [−1,+1].

Define for n > 1, X_n = (−1)ⁿ⁺¹ √ n+√

n+ 1

X₁. Thus we have S_n =

(−1)ⁿ⁺¹√

nX₁andn⁻¹IES²_n≤1. Intuitively, the oscillations of (ξ_n) are too vi- olent. To precise this idea, we will show the necessary condition (ii) in theorem 3 is violated by the subsequence (ξn_j)_j≥1wherenj = 2^j−1. Going back to (32) withp= 2 and expanding the square instead of applying the Jensen inequality, we obtain:

kξn_j −Ejξn_jk²₂ =

nj−1

X

i=1

Si−S_i−1

√n_j 2

2^2j i

n_j − ki

2^j

ki+ 1 2^j − i

n_j 2

+ ki+ 1

2^j − i nj

i nj

− ki

2^{j 2}

= 2^j nj

X₁²

nj−1

X

i=1

√ i+√

i−12 i nj

−ki

2^j

ki+ 1 2^j − i

nj

. (37) Restricting the summation in (37) to theiverifying 2^j/4≤ki≤3 2^j/4, we get a lower bound. Observing now that

i(2^j−1)⁻¹−k2^−j

is minimal fori−k=−1 or 0, we have:

min

1

42^j≤k≤³₄2^j

i 2^j−1 − k

2^j

= min

1

42^j≤k≤³₄2^j

min(k,2^j−k)

(2^j−1)2^j ≥ 1 4(2^j−1). Hence for ¹₄2^j ≤ki ≤³₄2^j:

i nj

−ki

2^j

ki+ 1 2^j − i

nj

≥ C 2^2j and

kξn_j−Ejξn_jk²₂≥ C

2^2jX₁² X

i:¹₄2^j≤ki≤³₄2^j

i.

Recall the integersk_i verify:

k_i 2^j ≤ i

2^j−1 <k_i+ 1

2^j , i= 1, . . . , n−1.

So we have forj≥2:

X

i:¹₄2^j≤ki≤³₄2^j

i ≥ X

i:¹₄2^j<i<³₄2^j

i ≥C⁰2^2j.

Finally,

kξn_j −Ejξn_jk²₂≥C⁰⁰X₁², j≥2.

Clearly, this contradicts condition (ii) of theorem 3.

4.4 Empirical process under strong mixing in L

(0, 1)

Let (Xn)_n≥1be a strictly stationary sequence of uniform variables on [0,1]. The strong mixing coefficients of this sequence are defined by:

αn = sup

|P(A∩B)−P(A) P(B)|, A∈ F₀^k+1, B∈ F_n+k^+∞, k∈IN , (38) where F_j^l stands for theσ-field generated by the variables (X_i, j < i < l). We define the empirical processes related to (Xn)_n≥1 by:

ξn(t) = 1

√n

n

X

i=1

1I_[Xi,1](t)−t

, t∈[0,1], n≥1. (39) In the D(0,1) setting, under the assumption:

α_n=O n^−3−ε

, ε >0. (40)

Yoshihara [23] proved the weak convergence of the empirical processξ_n to the centered gaussian process with covariance:

Γ(s, t) =s∧t−st+ 2

+∞

X

k=2

P(X1≤s, Xk ≤t)−st

. (41)

Recently, Oliveira and Suquet [16] proved the tightness of (ξn)_n≥1 in L²(0,1) and its convergence to a gaussian process with covariance Γ given by (41) under the assumption:

X

n≥1

αn<+∞ (42)

Thus there is some interest in investigating the tightness of (ξ_n)_n≥1between the two conditions (40) and (42) and the two spaces L²(0,1) andD(0,1).

Theorem 10 Suppose the mixing coefficients of(X_i)_i≥1 verify:

|αi| ≤ C

i^3−ε, i≥1, (43)

for some ε (0 ≤ε < 2) and some constant C. Then the sequence (ξn)_n≥1 of empirical processes defined by (39) is tight inL^p(0,1) for each p <6−2ε.

Regarding ξn as a random function defined on IR with support [0,1], we can use the theorem 7. Of course, in this particular case, the hypothesis (ii) of this later theorem is trivially verified. Our main tool will be the following theorem.

Theorem 11 (Yokoyama[24]) Let(Yi)_i≥1be a strictly stationary strong mix- ing sequence with IEY1= 0 andIE|Y1|^p+δ <+∞for somep >2 andδ >0. If its strong mixing coefficientsαi(Y), i≥1, verify:

+∞

X

i=1

(i+ 1)^p/2−1α_i(Y)^δ/(p+δ)<+∞, (44)