Convergence and regularity of probability laws by using an interpolation method

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Convergence and regularity of probability laws by using an interpolation method

Vlad Bally, Lucia Caramellino

To cite this version:

Vlad Bally, Lucia Caramellino. Convergence and regularity of probability laws by using an inter- polation method. Annals of Probability, Institute of Mathematical Statistics, 2017, 45 (2). �hal- 01109276v2�

Convergence and regularity of probability laws by using an interpolation method

Vlad Bally^∗ Lucia Caramellino^†

Abstract

Fournier and Printems [Bernoulli, 2010] have recently established a methodology which allows to prove the absolute continuity of the law of the solution of some stochastic equations with H¨older continuous coefficients. This is of course out of reach by using already classical probabilistic methods based on Malliavin calculus. By employing some Besov space techniques, Debussche and Romito [Probab. Theory Related Fields, 2014] have substantially improved the result of Fournier and Printems. In our paper we show that this kind of problem naturally fits in the framework of interpolation spaces: we prove an interpolation inequality (see Proposition 2.5) which allows to state (and even to slightly improve) the above absolute continuity result. Moreover it turns out that the above interpolation inequality has applications in a completely different framework: we use it in order to estimate the error in total variance distance in some convergence theorems.

AMS:46B70, 60H07.

Keywords: Regularity of probability laws, Orlicz spaces, Hermite polynomials, interpolation spaces, Malliavin calculus, integration by parts formulas.

1 Introduction

In this paper we prove an interpolation type inequality which leads to three main applications. First we give a criteria for the regularity of the law µ of a random variable. This was the first aim of the integration by parts formulas constructed in the Malliavin calculus (in the Gaussian framework, and of many other variants of this calculus, in a more general case). But our starting point was the paper of N. Fournier and J. Printems [18] who noticed that some regularity of the law may be obtained even if no integration by parts formula holds forµitself: they just use a sequenceµn→µand assume that an integration by parts formula of typeR

f^′dµ_n=R

f h_ndµ_nholds for eachµ_n.If sup_nR

|h_n|dµ_n<∞we are close to Malliavin calculus. But the interesting point is that one may obtain some regularity forµ even if sup_nR

|hn|dµn=∞- so we are out of the domain of application of Malliavin calculus. The key point is that one establishes an equilibrium between the speed of convergence ofµ_n→µand the blow up R

|h_n|dµ_n↑ ∞.The approach of Fournier and Printems is based on Fourier transforms and more

∗Universit´e Paris-Est, LAMA (UMR CNRS, UPEMLV, UPEC), MathRisk INRIA, F-77454 Marne-la-Vall´ee, France.

Email: bally@univ-mlv.fr

†Dipartimento di Matematica, Universit`a di Roma “Tor Vergata”, and INDAM-GNAMPA, Via della Ricerca Scien- tifica 1, I-00133 Roma, Italy. Email: caramell@mat.uniroma2.it.

recently Debussche and Romito [11] obtained a much more powerful version of this type of criteria based on Besov space techniques. This methodology has been used in several recent papers (see [5], [6], [7], [12], [10] and [17]) in order to obtain the absolute continuity of the law of the solution of some stochastic equations with weak regularity assumptions on the coefficients: as a typical example, one proves that, under uniform ellipticity conditions, diffusion processes with H¨older continuous coefficients have absolute continuous law at any time t > 0. In the present paper we use a different approach, based on an interpolation argument and on Orlicz spaces, which allows one to go further and to treat, for example, diffusion processes with log-H¨older coefficients.

The second application concerns the regularity of the density with respect to a parameter. We illustrate this direction by giving sufficient conditions in order that (x, y) 7→ p_t(x, y) is smooth with respect to (x, y) where p_t(x, y) is the density of the law of X_t(x) which is a piecewise deterministic Markov process starting fromx.

The third application concerns estimates of the speed of convergence µ_n → µ in total variation distance, and under some stronger assumptions, the speed of convergence of the derivatives of the densities of µ_n to the corresponding derivative of the density of µ. Such results appear in a natural way as soon as the suited interpolation framework is settled.

Let us give our main results. We work with the following weighted Sobolev norms onC^∞(R^d;R):

kfkk,m,p= X

0≤|α|≤k

Z (1 +|x|)^m|∂_αf(x)|^pdx1/p

, p >1,

where α is a multi index, |α| denotes its length and ∂_α is the corresponding derivative. In the case m= 0 we have the standard Sobolev norm that we denote bykfkk,p.We will also consider the weaker norm

kfkk,m,1+= X

0≤|α|≤k

Z

(1 +|x|)^m|∂_αf(x)|(1 + ln⁺|x|+ ln⁺|f(x)|)dx,

with ln⁺(x) = max{0,ln|x|}. Moreover, for two measures µand ν we consider the distances d_k(µ, ν) = supn

Z

φdµ− Z

φdν: X

0≤|α|≤k

k∂_αφk∞≤1o .

Fork= 0 this is the total variation distance and for k= 1 this is the Fortet Mourier distance.

Our key estimate is the following. Letm, q, k∈Nand p >1 be given and letp∗ be the conjugate of p.We consider a function φ∈C^q+2m(R^d) and a sequence of functionsφn∈C^q+2m(R^d), n∈N and we denoteµ(dx) =φ(x)dx and µ_n(dx) =φ_n(x)dx. We prove that there exists a universal constantC such that

kφkq,p≤CX^∞

n=0

2^{n(q+k+d/p∗)}d_k(µ, µ_n) + X∞ n=0

1

2^2mnkφ_nkq+2m,2m,p

(1.1)

and

kφkq,1+≤CX^∞

n=0

n2^n(q+k)d_k(µ, µ_n) + X∞ n=0

1

2^2mnkφ_nkq+2m,2m,1+

. (1.2)

This is Proposition 2.5 and the proof is based on a development in Hermite series and on a powerful estimate for mixtures of Hermite kernels inspired from [27]. This inequality fits in the general theory

of interpolation spaces (we thank to D. Elworthy for a useful remark in this sense). Many interpolation results between Sobolev spaces of positive and negative indexes are known but they are not relevant from a probabilistic point of view: convergence in distribution is characterized by the Fortet Mourier distance and this amounts to convergence in the dual ofW^1,∞.So we are not concerned with Sobolev spaces associated to L^p norms but toL^∞ norms. This is a limit case which is more delicate and we have not found in the literature classical interpolation results which may be used in our framework.

Once we have (1.1) and (1.2) we obtain the following regularity criteria. Let µ be a finite non negative measure. Suppose that there exists a sequence of functionsφn∈C^q+2m(R^d), n∈Nsuch that

d_k(µ, µn)× kφnk^αq+2m,2m,p ≤C, α > q+k+d/p_∗

2m . (1.3)

withµn(dx) =φn(x)dx. Then µ(dx) =φ(x)dxand φ∈W^q,p (the standard Sobolev space).

In terms ofkφkq,m,1+ the statement is the following: suppose that there existsm∈Nsuch that d₁(µ, µ_n)× kφ_nk^1/2m2m,2m,1+ ≤ C

(lnn)^2+1/2m. (1.4)

Then µis absolutely continuous with respect to the Lebesgue measure.

The statement of the corresponding results are Theorem 2.10 A and Theorem 2.9 respectively.

These are two significant particular cases of a more general result stated in terms of Orlicz norms in Theorem 2.6. The proof is, roughly speaking, as follows: let γε be the Gaussian density of variance ε >0 and let µ^ε=µ∗γ_ε and µ^ε_n=µ_n∗γ_ε.Then µ^ε(dx) =φ^ε(x)dxand µ^ε_n(x) =φ^ε_n(x)dx.Using (1.1) forφ^ε andφ^ε_n, n∈None proves that sup_εkφ^εkq,p<∞.And then one employs an argument of relative compactness in W^q,p in order to produce the density φofµ.

We give now the convergence result (see Theorem 2.11). Suppose that (1.3) holds for some α >

q+k+d/p∗

m .Then µ(dx) =φ(x)dxand, for every n∈N, kφ−φ_nkW^q,p ≤Cd^θ_k(µ, µ_n) with θ= 1

α ∧(1−q+k+d/p_∗

αm ). (1.5)

Roughly speaking this inequality is obtained by using (1.1) withµreplaced by µ−µ_n.

In the statements of (1.3) we do not used_k(µ, µ_n) andkφ_nk_q+2m,2m,p directly, but some functionλ having some nice properties such that λ(1/n)≥ kφ_nk1+q+2m,2m,p. But this is a technical point which we leave out in this introduction.

The paper is organized as follows. In Section 2 we introduce the Orlicz spaces, we give the general result and the criteria concerning the absolute continuity and the regularity of the density. We also give in Section 2.5 the convergence criteria mentioned above. In Section 2.6 we translate the results in terms of integration by parts formulae. In Section 3.1 (respectively Section 3.2) we prove absolute continuity for the law of the solution to a SDE (respectively to a SPDE) with log-H¨older continuous coefficients. Moreover, in Section 3.3 we discuss an example concerning piecewise deterministic Markov processes: we assume that the coefficients are smooth and we prove existence of the density of the law of the solution together with regularity with respect to the initial condition. We also consider an approximation scheme and we use (1.5) in order to estimate the error. Finally, we add some appendices containing technical results: Appendix A is devoted to the proof of the main estimate (1.1) based on a development in Hermite series; in Appendix B we discuss the relation with interpolation spaces; in Appendix C we give some auxiliary estimates concerning super kernels.

2 Criterion for the regularity of a probability law

2.1 Notations

We work onR^dand we denote byMthe set of the finite signed measures onR^dwith the Borelσalgebra.

Moreover M^a⊂ M is the set of the measures which are absolutely continuous with respect to the Lebesgue measure. Forµ∈ Mawe denote byp_µthe density ofµwith respect to the Lebesgue measure.

And for a measureµ∈ Mwe denote byL^p_µthe space of the measurable functionsf :R^d→Rsuch that R |f|^pd|µ|< ∞.For f ∈ L¹_µ we denote f µ the measure (f µ)(A) = R

Af dµ. For a bounded function φ:R^d→Rwe denoteµ∗φthe measure defined byR

f dµ∗φ=R

f∗φdµ=R R

φ(x−y)f(y)dydµ(x).

Then µ∗φ∈ Maand p_µ∗φ(x) =R

φ(x−y)dµ(y).

We denote byα= (α₁, ..., α_d)∈N^da multi index and we put |α|=Pd

i=1α_i.HereN={0,1,2, ...} is the set of non negative integers and we put N_∗ = N\ {0}. For a multi index α with |α| = k we denote ∂_α the corresponding derivative that is∂_x^α₁¹...∂_x^α_d^d with the convention that ∂_x^α_iⁱf =f if α_i = 0.

In particular ifα is the null multi index then∂_αf =f.

We denote bykfkp = (R

|f(x)|^pdx)^1/p, p≥1 and kfk∞= sup_x∈Rd|f(x)|.Then L^p ={f :kfkp <

∞}are the standard L^p spaces with respect to the Lebesgue measure.

2.2 Orlicz spaces

In the following we will work in Orlicz spaces, so we briefly recall the notations and the results we will use, for which we refer to [20].

A function e : R → R₊ is said to be a Young function if it is symmetric, strictly convex, non negative ande(0) = 0.In the following we will consider Young functions having the two supplementary properties:

i) there exists λ >0 such that e(2s)≤λe(s), ii) s7→ e(s)

s is non decreasing. (2.1)

The property i) is known as the ∆₂ condition or doubling condition (see [20]). Through the whole paper we work with Young functions which satisfy (2.1). We set E the space of these functions:

E ={e : eis a Young function satisfying (2.1)}. (2.2) Fore∈ E andf :R^d→R, we define the norm

kfke= infn c >0 :

Z e1

cf(x)

dx≤1o

. (2.3)

This is the so called Luxembourg norm which is equivalent to the Orlicz norm (see [20] p 227 Th 7.5.4). It is convenient for us to work with this norm (instead of the Orlicz norm). The space L^e={f :kfke<∞}is the Orlicz space.

Remark 2.1. Let u_l(x) = (1 +|x|)^−l. As a consequence of (2.1) ii), for every e∈ E and l > d one has u_l∈L^e and moreover,

ku_lke≤(e(1)ku_lk₁)∨1<∞. (2.4) Indeed (2.1) ii) implies that for t≤1 one has e(t)≤e(1)t. For c≥(e(1)ku_lk1)∨1 one has ¹_cu_l(x)≤ u_l(x)≤1 so that Z

e1 cu_l(x)

dx≤ e(1) c

Z

u_l(x)dx= e(1)

c ku_lk1 ≤1.

Fora >0, we define e⁻¹(a) = sup{c:e(c)≤a} and:

φe(r) = 1

e^{−1 1}_r and βe(R) = R

e⁻¹(R) =Rφe

1 R

, r, R >0. (2.5)

Remark 2.2. The function φe is the “fundamental function” of L^e equipped with the Luxembourg norm (see [9] Lemma 8.17 pg 276). In particular ¹_rφe(r) is decreasing (see [9] Corollary 5.2 pg 67).

It follows thatβe is increasing. For the sake of completeness we give here the argument. By (2.1), ii), if a > 1 then e(ax)≥ae(x) so that ax≥e⁻¹(ae(x)). Taking y =e(x) we obtain ae⁻¹(y)≥e⁻¹(ay) which gives

βe(ay) = ay

e⁻¹(ay) ≥ ay

ae⁻¹(y) =βe(y).

One defines the conjugate ofeby

e_∗(s) = sup{st−e(t) :t∈R}.

e_∗ is a Young function as well, so the corresponding Luxembourg normkfke_∗ is given by (2.3) withe replaced bye_∗. And one has the following H¨older inequality:

Z

f g(x)dx

≤2kfkekgke_∗. (2.6)

(see Theorem 7.2.1 page 215 in [20]; we stress that the factor 2 does not appear in that reference but in the right hand side of the inequality in the statement of Theorem 7.2.1 in [20] one has the Orlicz norm ofgand by using the equivalence between the Orlicz and the Luxembourg norm we can replace the Orlicz norm by 2kgke_∗).

We will now define Sobolev norms and Sobolev spaces associated to an Young function e. Let us denote by L¹_loc the space of measurable functions which are integrable on compact sets and by W_loc^k,1 the space of measurable functions which are k times weakly differentiable and have locally integrable derivatives. More precisely this means that f ∈ W_loc^k,1 if for every multi index α with

|α| ≤ k one may find a function f_α ∈L¹_loc (determined dx almost surely) such that R

g(x)f_α(x)dx= (−1)^|α|R

∂_αg(x)f(x)dxfor every g∈C_c^∞(R^d,R).In this case we denote ∂_αf =f_α.Notice that

L^e⊂L¹_loc. (2.7)

In order to prove this we take R >0 and we notice that for |x| ≤R one has (1 +R)^d+1u_d+1(x)≥1.

Then using (2.6) and (2.4), for everyf ∈L^e Z

BR

|f(x)|dx≤(1 +R)^d+1 Z

R^d

u_d+1(x)|f(x)|dx

≤(1 +R)^d+1ku_d+1ke∗kfke<∞. Forf ∈W_loc^k,1, we introduce the norms

kfkk,e= X

0≤|α|≤k

k∂_αfke and kfkk,∞= X

0≤|α|≤k

k∂_αfk∞ (2.8)

and we denote

W^k,e={f ∈W_loc^k,1:kfkk,e<∞}and W^k,∞={f ∈W_loc^k,1:kfkk,∞<∞}. For a multi indexγ we denotex^γ =Qd

i=1x^γ_iⁱand for two multi indexesα, γwe denotef_α,γthe function f_α,γ(x) =x^γ∂_αf(x).

Then we consider the norms kfkk,l,e= X

0≤|α|≤k

X

0≤|γ|≤l

kf_α,γke and W^k,l,e={f :kfkk,l,e<∞}. (2.9) We stress that in k · kk,l,e the first index k is related to the order of the derivatives which are involved while the second indexlis connected to the power of the polynomial multiplying the function and its derivatives up to order k.

Finally we recall that if e satisfies the ∆₂ condition (that is (2.1) i)) then L^e is reflexive (see [20], Theorem 7.7.1, p 234). In particular, in this case, any bounded subset ofL^eis weakly relatively compact.

Let us propose two examples of Young functions, that represent the leading ones in our approach.

Example 1. If we takee_p(x) =|x|^p, p >1,thenkfk^e_p is the usualL^pnorm and the corresponding Orlicz space is the standardL^p space onR^d. Clearly βe_p(t) =t^1/p∗ with p_∗ the conjugate of p.

Example 2. Sete_log(t) = (1 +|t|) ln(1 +|t|).Since the norm frome_log is not explicit we replace it by the following quantities:

kfkp,1+= Z

(1 +|x|)^p|f(x)|(1 + ln⁺|x|+ ln⁺|f(x)|)dx kfkk,p,1+= X

0≤|α|≤k

k∂_αfkp,1+

(2.10)

with ln⁺(x) = max{0,ln|x|}. We stress thatkfkp,1+ is not a norm.

We will need the following:

Lemma 2.3. For each k∈N andp≥0 there exists a constant C depending on k, p only such that kfkk,p,e_log ≤C(1∨ kfkk,p,1+). (2.11) Moreover

lim sup

t→∞

βe_log(t)

lnt ≤2. (2.12)

Proof. The inequality (2.11) is an immediate consequence of the following simpler one:

kfke_log ≤2 1∨

Z

|f(x)|(1 + ln⁺|f(x)|)dx

. (2.13)

Let us prove it. We assume thatf ≥0 and we take c≥2 and we write Z

e_log1 cf(x)

dx≤ Z

{f≤c}

e_log1 cf(x)

dx+ Z

{f >c}

e_log1 cf(x)

dx=:I+J.

Using the inequality ln(1 +y)≤ywe obtainI ≤2R

ln(1 +¹_cf)≤ ²_c R

f.And iff ≥c≥2 then ^f_c + 1≤

2

cf ≤ f. Then e_log(¹_cf(x)) ≤ ²_cflnf. It follows that J ≤ ²_c R

{f >c}fln⁺f and finally R

e_log(¹_cf)) ≤

2 c

R

{f >c}(1 +f) ln⁺f.We conclude that for c≥2R

f(1 + ln⁺f) we have R

e_log(¹_cf)≤1 which by the very definition means thatkfke_log ≤2R

f(1 + ln⁺f).

Let us prove (2.12). We denotee(t) = 2tln(2t) and we notice that for largetone hase_log(t)≤e(t).

It follows that

βe_log(t)≤ t e⁻¹(t). Using the change of variableR =e(t) we obtain

R→∞lim

R

e⁻¹(R) lnR = lim

t→∞

e(t) tlne(t) = 2.

So for large R we have βe_log(R)≤R/e⁻¹(R)≤2 lnR.

Remark 2.4. We recall that the LlogL space of Zygmund is the space of the functions f such that R |f(x)|ln⁺|f(x)|dx <∞(see [9]). Then L^elog =L¹∩LlogL. The inequality (2.13) already gives one inclusion. The converse inclusion is a consequence of the following inequalities. Let ε_∗ > 0 be such that t≤2 ln(1 +t) for 0< t≤ε_∗ and let C_∗ = 2 + 1/ln(1 +ε_∗).Then

i)

Z

|f(x)|dx≤C_∗kfke_log and ii)

Z

|f(x)|ln⁺|f(x)|dx≤ kfke_log(1 + 2C_∗ln⁺kfke_log).

(2.14)

In order to prove i) we denote g=kfk^−1e_log|f|and we write Z

g= Z

{g≤ε∗}

g+ Z

{g>ε∗}

g

≤2 Z

{g≤ε∗}

ln(1 +g) + 1 ln(1 +ε_∗)

Z

{g>ε∗}

gln(1 +g)

≤C_∗ Z

(1 +g) ln(1 +g) =C_∗ Z

e_log(g) =C_∗. In order to prove ii) we notice that R

gln⁺g≤R

e_log(g) = 1so that Z

|f|ln⁺ |f| kfke_log

≤ kfk^e_log. Then we write

Z

|f|ln⁺|f|= Z

{|f|≥1∨kfkelog}|f|ln⁺|f|+ Z

{|f|<1∨kfkelog}|f|ln⁺|f|

=:I+J.

If|f| ≥1∨kfke_log thenln⁺|f|= ln|f|= ln⁺(_kf^|f_k^|

elog) + lnkfk^elog. So, by using the previous inequality, I ≤ kfke_log + lnkfke_log

Z

|f| ≤ kfke_log(1 +C_∗lnkfke_log) the last inequality being a consequence of i).And

J ≤ln⁺kfk^e_log Z

|f| ≤C_∗kfk^e_logln⁺kfk^e_log. 2.3 Main results

We consider the following distances between two measures µ, ν ∈ M: fork∈N, we set d_k(µ, ν) = supn

Z

φdµ− Z

φdν:φ∈C^∞(R^d),kφkk,∞≤1o

. (2.15)

Notice thatd₀is the total variation distance andd₁is the bounded variation distance (also called Fortet Mourier distance) which is related to the convergence in law of probability measures. The distances d_k withk≥2 are less often used. We mention however that people working in approximation theory (for diffusion process for example - see [30] or [24]) use such distances in an implicit way: indeed, they study the speed of convergence of certain schemes but they are able to obtain their estimates for test functionsf ∈C^kwithksufficiently large - sod_kcomes on. We also recall that fork= 1,2,3,d_kplays an important role in the so-called Stein’s method for normal approximation (see e.g. [25]).

We fix now a Young functione∈ E (see (2.2)), and we recall the functionβe(see (2.5) and Remark 2.2 respectively).

Letq, k∈Nand m∈N_∗.For µ∈ M and for a sequenceµ_n∈ Ma, n∈Nwe define π_q,k,m,e(µ,(µn)n) =

X∞ n=0

2^n(q+k)βe(2^nd)d_k(µ, µn) + X∞ n=0

1

2^2nmkpµnk2m+q,2m,e. (2.16) Here and in the sequel we make the convention thatkp_µnk_2m+q,2m,e=∞ifp_µn ∈/W_loc^2m+q,1. Moreover we define

ρ_q,k,m,e(µ) = infπ_q,k,m,e(µ,(µ_n)_n) (2.17) the infimum being over all the sequences of measuresµ_n, n∈Nwhich are absolutely continuous. It is easy to check that ρ_q,k,m,e is a norm on the spaceSq,k,m,e defined by

Sq,k,m,e={µ∈ M:ρ_q,k,m,e(µ)<∞}. (2.18) The following result gives the key estimate in our paper. We prove it in Appendix A.

Proposition 2.5. Let q, k ∈N, m ∈N_∗ and e ∈ E. There exists a universal constant C (depending on q, k, m, d and e) such that for every f ∈C^2m+q(R^d) one has

kfkq,e≤Cρ_q,k,m,e(µ) (2.19)

where µ(dx) =f(x)dx.

We state now our main theorem:

Theorem 2.6. Let q, k∈N, m∈N_∗ and let e∈ E. i) Take q = 0.Then

S0,k,m,e⊂L^e

in the sense that if µ ∈ S0,k,m,e then µ is absolutely continuous and the density p_µ belongs to L^e. Moreover there exists a universal constant C such that

kp_µk_L^e ≤Cρ_0,k,m,e(µ).

ii) Take q≥1.Then

Sq,k,m,e⊂W^q,e and kp_µk_q,e≤Cρ_q,k,m,e(µ), µ∈ Sq,k,m,e. Proof. We consider a functionφ∈C_b^∞(R^d) such that 0≤φ≤1_B1 and R

Rφ(x)dx= 1. For δ∈(0,1), we define φ_δ(x) = δ^−dφ(δ⁻¹x). For a measure µ we define µ∗φ_δ by R

f dµ∗φ_δ = R

f ∗φ_δdµ. Since kf ∗φ_δkk,∞≤ kfkk,∞it follows that d_k(µ∗φ_δ, ν ∗φ_δ)≤d_k(µ, ν).We will also prove that

kf∗φ_δk_2m+q,2m,e≤2^2mkfk_2m+q,2m,e. (2.20) Suppose for a moment that (2.20) holds. Then

π_q,k,m,e(µ∗φ_δ,(µ_n∗φ_δ)_n)≤2^2mπ_q,k,m,e(µ,(µ_n)_n)≤2^2mρ_q,k,m,e(µ).

Letp_δ∈C^∞(R^d,R) be the density of the measureµ∗φ_δ.The above inequality and (2.19) prove that sup

0<δ≤1kp_δkq,e≤Cρ_q,k,m,e(µ)<∞.

For each multi indexα with |α| ≤q the family ∂αp_δ, δ ∈(0,1) is bounded inL^e which is a reflexive space, so it is weakly relatively compact. Then we may find p_α ∈ L^e ⊂ L¹_loc (see (2.7) for the above inclusion) and a sequence δ_n → 0 such that ∂_αp_δn → p_α weakly, for every multi index α with 0 ≤ |α| ≤ q (in the same time). Since R

g∂αp_δn = (−1)^|α|R

p∂αg, by passing to the limit we obtain R

gp_α = (−1)^|α|R

p∂_αg so ∂_αp = p_α ∈ L^e and this means that p ∈W^q,e. Since µ∗φ_δn → µ weakly, one has µ(dx) =p(x)dx. And sincek∂_αpke≤sup_n∈Nk∂_αp_δnke≤Cρ_q,k,m,e(µ) it follows that kpkq,e≤Cρ_q,k,m,e(µ).So the proof is completed.

Let us check (2.20). Forλ >0 we denote g_λ(x) = (1 +|x|)^λg(x).Notice that for δ ≤1

|(g∗φ_δ)_λ(x)| ≤ (1 +|x|)^λ Z

|g(x−y)|φ_δ(y)dy

≤ Z

(1 +|x−y|+δ)^λ|g(x−y)|φ_δ(y)dy

≤ 2^λ Z

(1 +|x−y|)^λ|g(x−y)|φ_δ(y)dy= 2^λ|g_λ| ∗φ_δ(x).

Then, by (A.6) k(g∗φ_δ)_λk^e ≤ 2^λk|g_λ| ∗φ_δk^e ≤ 2^λkφ_δk1k|g_λ|k^e = 2^λkg_λk^e. Using this inequality (with λ= 2m) forg=∂_αf we obtain (2.20).

We consider now a special class of Orlicz norms which verify a supplementary condition: given α, γ ≥0 we define

E^α,γ =n

e: lim sup

R→∞

βe(R)

R^α(lnR)^γ <∞o

. (2.21)

In this case we have:

Theorem 2.7. Let q, k∈N, m∈N_∗ and let e∈ Eα,γ. If 2m > d, γ≥0 and 0≤α < ^2m+q+k_d(2m−1) then W^q+1,2m,e⊂ Sq,k,m,e⊂W^q,e

and there exists some constant C such that 1

Ckpµkq,e≤ρ_q,k,m,e(µ)≤Ckpµkq+1,2m,e. (2.22) In particular this is true for e_log and for e_p with ^p−1_p < ^2m+q+k_d(2m−1).

Proof. The first inequality in (2.22) is proved in Theorem 2.6. As for the second, we use Lemma C.3 in Appendix C. Let f ∈ W^q+1,2m,e and µ_f(dx) = f(x)dx. We have to prove that ρ_q,k,m,e(µ_f) < ∞. We consider a super kernel φ (see (C.1)) and we define f_δ = f ∗φ_δ. We take δ_n = 2^−θn with θ to be chosen in a moment and we choose n_∗ such that for n≥ n_∗ one has βe(2^nd) ≤ C2^ndαn^γ because e∈ Eα,γ. Using (C.3) with l= 2m, we obtain d_k(µ_f, µ_fδn)≤C_k,qkfkq+1,2m,eδ^q+k+1n and using (C.4) we obtainkf_δnk2m+q,2m,e≤C_2m+q,2mkfkq+1,2m,eδ^−(2m−1)_n (the constantCdepends onkandq,which are fixed). Then we can write

π_q,k,m,e(µ_f, µ_fδn)

= X∞ n=0

2^n(q+k)βe(2^nd)d_k(µ_f, µ_fδn) + X∞ n=0

1

2^2nmkf_δnk_2m+q,2m,e

≤C_q,k,mkfkq+1,2m,e×

× 1 +

X∞ n≥n∗

2n(q+k+dα−θ(q+k+1))n^γ+ X∞ n=0

1 2n(2m−θ(2m−1))

,

C_q,k,m>0 denoting a constant depending onq, k, m. In order to obtain the convergence of the above series we need to chooseθ such that

q+k+dα

q+k+ 1 < θ < 2m 2m−1 and this is possible under our restriction onα.

We give now a criterion in order to check thatµ∈ Sq,k,m,e.

Theorem 2.8. Letq, k∈N, m∈N_∗ and lete∈ E^α,γ. We consider a non negative finite measure µand we suppose that there exists a family of measures µ_δ(dx) =f_δ(x)dx, δ >0 which verifies the following assumptions. There exist C, r > 0 and a function λ_q,m(δ), δ ∈(0,1), which is right-continuous and non increasing such that

kf_δk2m+q,2m,e≤λ_q,m(δ)≤Cδ^−r.

We consider some η >0 and κ≥0 and we assume that λ^η_q,m(δ)d_k(µ, µ_δ)≤ C

(ln(1/δ))^κ. (2.23)

If (2.23) holds with

η > q+k+αd

2m , κ= 0 (2.24)

then

µ∈ Sq,k,m,e⊂W^q,e. The same conclusion holds if

η = q+k+αd

2m and κ >1 +γ+η. (2.25)

Proof. Let ε₀ >0.We define

δn= inf{δ >0 :λq,m(δ)≤ 2^2mn n^1+ε0}.

Let 0< θ <2m/r where r is the one in the growth condition onλ_q,m.Sinceδ^rλ_q,m(δ)≤C, we have λ_q,m(2^−θn)≤C2^nθr≤ 2^2mn

n^1+ε0 which means thatδn≤2^−θn.Since e∈ E^α,γ we have

π_q,k,m,e(µ,(µ_δn)_n)

≤C X∞ n=1

2^n(q+k+αd)n^γd_k(µ, µ_δn) +C X∞ n=1

2^−2mnkf_δnk2m+q,2m,e. Since λ_q,m is right continuous, λ_q,m(δ_n) = 2^2mnn^−(1+ε0) so

X∞ n=1

1

2^2mnλ_q,m(δ_n)<∞. By recalling that ln(1/δ_n)≥Cθn and by using (2.23), we obtain

2^n(q+k+αd)n^γd_k(µ, µ_δn) ≤2^n(q+k+αd)_λη ^Cnγ q,m(δn)(ln(1/δn))^κ

≤C×2n(q+k+αd−2mη)n^{γ+η(1+ε0)−κ}. (2.26) Ifq+k+αd <2ηmthe series with the general term given in (2.26) is convergent. Ifq+k+αd= 2ηmn we need thatκ >1 +γ+η(1 +ε₀) in order to obtain the convergence of the series. If κ >1 +γ+η then we may choose ε₀ sufficiently small in order to haveγ+η(1 +ε₀)−κ >1 and we are done.

There are two important examples: e = e_p that we discuss in a special subsection below and e=e_log which we discuss now. We recall that e_log ∈ Eα,γ withα= 0 andγ = 1 and kf_δk2m,2m,e_log ≤ C1∨ kf_δk2m,2m,1+ where kf_δk2m,2m,1+ is defined in (2.10). Then as a particular case of the previous theorem we obtain:

Theorem 2.9. We consider a non negative finite measure µand we suppose that there exists a family of measures µ_δ(dx) = f_δ(x)dx, δ > 0 which verifies the following assumptions. There exist m ∈ N_∗, C, r, ε >0 and a function λ_m(δ), δ ∈(0,1), which is right-continuous and non increasing such that

kf_δk2m,2m,1+ ≤λ_m(δ)≤Cδ^−r and λ_m^2m1 (δ)d₁(µ, µ_δ)≤ ^C

(ln(1/δ))^{2+ 12m+ε}. (2.27)

Then µ(dx) =f(x)dx withf ∈L^elog. 2.4 The L^p criterion

In the case of the L^p norms, that is e=e_p, our result fits in the general theory of the interpolation spaces and we may give a more precise characterization of the space Sq,k,m,e_p =: Sq,k,m,p. We come back to the standard notation and we denote k·kp instead ofk·ke_p, W^q,p instead ofW^q,ep and so on.

In Appendix B we prove that in this case the space Sq,k,m,p is related to the following interpolation space. LetX=W_∗^k,∞ whereW_∗^k,∞is the dual of W^k,∞(notice that one may look toµ∈ Mas to an element ofW_∗^k,∞and thend_k(µ, ν) =kµ−νk_W∗^k,∞).We also take Y =W^q+2m,2m,p and for γ ∈(0,1) we denote by (X, Y)_γ the real interpolation space of order γ betweenX and Y (see Appendix B for notations). Then we have

Sq,k,m,p = (X, Y)γ with γ = q+k+d/p∗

2m .

So Theorem 2.7 reads

W^q+1,2m,p⊂(W_∗^k,∞, W^q+2m,2m,p)_γ ⊂W^q,p.

We go now further and we notice that if (2.24) holds then the convergence of the series in (2.26) is very fast. This allows us to obtain some more regularity.

Theorem 2.10. Let q, k∈N, m∈N_∗, p >1 and set η > q+k+d/p∗

2m . (2.28)

We consider a non negative finite measure µ and a family of finite non negative measures µ_δ(dx) = f_δ(x)dx, δ >0.

A.We assume that there existC, r >0and a right-continuous and non increasing functionλ_q,m(δ), δ ∈(0,1), such that

kf_δk2m+q,2m,p ≤λ_q,m(δ)≤Cδ^−r and moreover, with η given in (2.28),

λ_q,m(δ)^ηd_k(µ, µ_δ)≤C. (2.29)

Then µ(dx) =f(x)dx withf ∈W^q,p.

B. We assume that (2.29) holds with q+ 1 instead of q,that is λ_q+1,m(δ)^ηd_k(µ, µ_δ)≤C.

We denote

s_η(q, k, m, p) = 2mη−(q+k+d/p_∗)

2mη ∧ η

1 +η. (2.30)

Then for every multi indexα with |α|=q and every s < s_η(q, k, m, p) we have ∂_αf ∈ B^s,p where B^s,p is the Besov space of index s.

Proof. A. The fact that (2.29) implies µ(dx) = f(x)dx with f ∈W^q,p is an immediate consequence of Theorem 2.8.

B.We prove the regularity property: g:=∂_αf ∈ B^s,p for|α|=q and s < s_η(q, k, m). In order to do it we will use Lemma B.1 so we have to check (B.4).

Step 1. We begin with the pointi) in (B.4) so we have to estimatekg∗∂_iφ_εk∞. The reasoning is analogous with the one in the proof of Theorem 2.8 but we will use the first inequality in (2.22) with q replaced byq+ 1 andk replaced byk−1.So we defineδ_n= inf{δ >0 :λ_q+1,m(δ)≤n⁻²2^2mn} and we have δ_n≤2^−θn forθ <2m/r.We obtain

kg∗∂_iφ_εkp = k∂_i∂_α(f∗φ_ε)kp ≤ kf ∗φ_εkq+1,p ≤ρq+1,k−1,m,p(µ∗φ_ε)

≤ X∞ n=1

2^{n(q+k+d/p∗)}d_k−1(µ∗φε, µ_δn∗φε) +

X∞ n=1

2^−2mnkf_δn∗φ_εk2m+q+1,2m,p. By the choice ofδ_n,

kf_δn∗φ_εk2m+q+1,2m,p ≤ kf_δnk2m+q+1,2m,p ≤λ_q+1,m(δ_n)≤ 1 n²2^2nm

so the second series is convergent. We estimate now the first sum. Sincekf ∗φεkk,∞≤ε⁻¹kfkk−1,∞, one hasd_k−1(µ∗φ_ε, µ_δn ∗φ_ε) ≤ε⁻¹d_k(µ, µ_δn). Then, using (2.29) (with q = 1 instead of q) and the choice ofδ_n we obtain

2^{n(q+k+d/p∗)}d_k−1(µ∗φε, µ_δn∗φε) ≤ C

ε2^{n(q+1+d/p∗)}d_k(µ, µ_δn)

≤ C

ε2^{n(q+1+d/p∗)}λ^−η_q+1,m(δ_n)

≤ Cn^2η

ε 2^{n(q+1+d/p∗−2mη)}. We fix nowε >0, we take some n_ε∈N(to be chosen in the sequel) and we write

X∞ n=1

2^{n(q+k+d/p∗)}d_k−1(µ∗φε, µ_δn∗φε)

≤C

nε

X

n=1

2^{n(q+k+d/p∗)}+ C ε

X∞ n=nε+1

n^2η2^{n(q+k+d/p∗−2ηm)}. We take a >0 and we upper bound the above series by

2^{nε(q+k+d/p∗)}+C

ε2^{nε(q+k+d/p∗+a−2ηm)}.

In order to optimize we taken_ε such that 2^2mnε = ¹_ε. With this choice we obtain 2^{nε(q+k+d/p∗+a)}≤Cε^{−q+k+d/p∗+a2mη} .

We conclude that

kg∗∂_iφ_εkp≤Cε^{−q+k+d/p∗+a2mη} which means (B.4)i) holds for s <1−^q+k+d/p_2mη ^∗.

Step 2. We check now (B.4) ii) so we have to estimate g∗φⁱ_ε

p withφⁱ_ε(x) =xⁱφ_ε(x). We take u∈(0,1) (to be chosen in a moment) and we define

δ_n,ε= inf{δ >0 :λ_q+1,m(δ)≤n⁻²2^2mn×ε^−(1−u)}. Then we proceed as in the previous step:

∂_i(g∗φⁱ_ε)_p ≤ρq+1,k−1,m,p(µ∗φⁱ_ε)

≤ X∞ n=1

2^{n(q+k+d/p∗)}d_k−1(µ∗φⁱ_ε, µ_δn,ε∗φⁱ_ε) +

X∞ n=1

2^−2mnf_δn,ε∗φⁱ_ε

2m+q+1,2m,p. It is easy to check that for every h ∈L^p one has h∗φⁱ_ε

p ≤εkhkp so that, by our choice of δ_n,ε we obtain

f_δn,ε∗φⁱ_ε

2m+q+1,2m,p≤εf_δn,ε

2m+q+1,2m,p ≤ε×2^2mn

n² ×ε^−(1−u). It follows that the second sum is upper bounded byCε^u.

Since∂_jh∗φⁱ_ε∞≤Ckhk∞it follows that

d_k−1(µ∗φⁱ_ε, µ_δn,ε∗φⁱ_ε)≤Cd_k(µ, µ_δn,ε)≤ C

λ^η_q+1,m(δ_n,ε) = Cn²

2^2mnηε^η(1−u).

Since 2mη > q+k+d/p_∗ the first sum is convergent also and is upper bounded by Cε^η(1−u). We conclude that ∂_i(g∗φⁱ_ε)

p ≤Cε^η(1−u)+Cε^u. In order to optimize we takeu= _1+η^η .

2.5 Convergence criteria in W^q,p and W^q,elog For a functionf, we denote µ_f(dx) =f(x)dx.

Theorem 2.11. Let η :R₊→R₊ be a non decreasing function and a≥1 be such that

n→∞lim η(n) = +∞ and η(n+ 1)≤aη(n), for every n∈N. (2.31) Let m, k, q ∈N be fixed. Letf_n, n∈N, be a sequence of functions andµ∈ M.

i) Let p≥1. If there exists α > ^q+k+d/p_m ^∗ such that

kf_nkq+2m,2m,p ≤η^1/α(n) and d_k(µ, µ_fn)≤ 1

η(n), (2.32)

then µ(dx) =f(x)dx for some f ∈W^q,p. Moreover, there exists a constant C depending on a, α such that for every n∈N

kf −f_nkq,p≤Cη^−θ(n) with θ= 1

α ∧(1−q+k+d/p_∗

αm ). (2.33)

ii) If there exists α > ^q+k_m such that

kf_nk_q+2m,2m,1+ ≤η^1/α(n) and d_k(µ, µ_fn)≤ 1

η(n), (2.34)

then µ(dx) = f(x)dx for some f ∈ W^q,elog. Moreover, there exists a constant C depending on a, α such that for every n∈N

kf −f_nkq,e_log ≤C(η^−1/α(n) + (log₂η(n))η^{−(1−q+kαm)}(n)) =:ε_n(α). (2.35) And if ε_n(α)≤1 then

X

0≤|α|≤q

Z

|(∂_αf−∂_αf_n)(x)|(1 + ln⁺|(∂_αf −∂_αf_n)(x)|dx≤2C_∗ε_n(α). (2.36) Proof. i) Step 1. Forr ∈N, we define

n_r= min{n:η(n)≥2^αrm} and r_n= min{r∈N:n_r≥n}. Then we have

1

aη(n)≤2^αrnm≤Cη(n). (2.37)

Since {r ∈ N : n_r ≥ n} is a discrete set, its minimum r_n belongs to this set, so n_rn ≥ n. Then η(n)≤η(n_rn) ≤aη(n_rn −1) ≤a2^αrnm.On the other hand, since r_n−1∈ {/ r ∈N:n_r ≥n} one has n > nrn−1 and thenη(n)≥η(nrn−1)≥2^α(rn−1)m=C⁻¹2^αrnm withC= 2^αm.So, (2.37) holds.

Step 2. We fixn∈Nand for r ∈Nwe define

g_r= 0 if r < r_n and g_r =f_nr−f_n if r≥r_n

and ν(dx) =µ(dx)−f_n(x)dx, ν_r(dx) =g_r(x)dx. Using (2.19) (recall thatβe_p =t^1/p∗) we get ρ_q,k,m,p(ν)≤

X∞ r=1

2^{r(q+k+d/p∗)}d_k(ν, ν_r) + X∞ r=1

2^−2mrkg_rkq+2m,2m,p =:S₁+S₂.

We estimateS₁.Forr < r_nwe haveν_r = 0 so thatd_k(ν, ν_r) =d_k(ν,0) =d_k(µ, µ_fn)≤η⁻¹(n).And forr≥rn we have

d_k(ν, ν_r) =d_k(µ, µ_fnr)≤ 1

η(nr) ≤ 1 2^rmα. So, we obtain

S₁≤2^{rn(q+k+d/p∗)}η⁻¹(n) + C

2^{rnmα(1−(q+k+d/p∗αm )} and using (2.37),

S₁ ≤Cη^{−(1−q+k+d/p∗αm )}(n).

We estimate nowS₂.We have g_r= 0 for r < r_nand for r ≥r_n

kg_rkq+2m,2m,p ≤ kf_nrkq+2m,2m,p+kf_nkq+2m,2m,p ≤η(n_r)^1/α+η(n)^1/α. But η(n_r)≤aη(n_r−1)≤a2^αrm, so that

kg_rk_q+2m,2m,p≤a^1/α2^rm+η(n)^1/α. It follows that

S₂≤a^1/α X

r≥rn

2^−rm+η(n)^1/α X

r≥rn

2^−2rm ≤C 2^−rnm+η(n)^1/α2^−2rnm and using (2.37) we get

S₂ ≤Cη(n)^−1/α. Then, we obtain

ρ_q,k,m,p(ν)≤C(η^−1/α(n) +η^{−(1−q+k+d/p∗αm )}(n)) and Theorem 2.6 allows one to conclude.

ii) We taken_r and r_n as in Step 1 above, giving (2.37), and we take g_r,ν,ν_r as in Step 2 above.

Then, by using (2.19) we get ρ_q,k,m,e_log(ν)≤

X∞ r=1

2^r(q+k)βe_log(2^rd)d_k(ν, νr) + X∞ r=1

2^−2mrkgrkq+2m,2m,e_log. By (2.11) and (2.12), we can write

ρ_q,k,m,e_log(ν)≤C X∞ r=1

2^r(q+k)rd_k(ν, ν_r) + X∞ r=1

2^−2mr1∨ kg_rkq+2m,2m,1+

=:S₁+S₂.

Concerning S₁, forr < r_n we have d_k(ν, ν_r) =d_k(ν,0) =d_k(µ, µ_fn)≤η⁻¹(n) and for r≥r_n we have d_k(ν, ν_r)≤ _η(n¹_r) ≤ ₂^rmα1 . So, we obtain

S₁≤C

r_n2^rn(q+k)η⁻¹(n) + r_n 2^{rnmα(1−q+kαm)}

.

Using (2.37),

S₁≤Cr_nη^{−(1−q+kαm)}(n)≤C(log₂η(n))η^{−(1−q+kαm)}(n).

As forS₂, we proceed as in Step 2 above and we obtain S₂ ≤Cη(n)^−1/α.Then, ρ_q,k,m,e_log(ν)≤C(η^−1/α(n) +η^{−(1−q+k+d/p∗αm )}(n))

and the statement again follows from Theorem 2.6. So (2.35) is proved. In order to check (2.36) we use (2.14) (notice that, sincekf −f_nkq,e_log ≤ε_n(α)≤1,we have ln⁺kf −f_nkq,e_log = 0).

2.6 Random variables and integration by parts

In this section we work in the framework of random variables. For a random variable F we denote by µ_F the law of F and if µ_F is absolutely continuous we denote by p_F its density. We will use Theorem 2.10 for µ_F so we will look for a family of random variables F_δ, δ >0 such that µ_Fδ satisfy the hypothesis of this theorem. Sometimes it is easy to construct such a family with explicit densities p_Fδ and then one may check (2.29) directly (this is the case in the examples in Section 3.1 and 3.2).

But sometimes one does not knowp_Fδ and then it is useful to use the integration by parts machinery in order to prove (2.29) - this is the case in the example given is Section 3.3 or the application to a kind of generalization of the H¨ormander condition to general Wiener functionals developed in [4].

We briefly recall the abstract definition of integration by parts formulae and we give some useful properties (coming essentially from [1]). We consider two random variables F = (F₁, ..., F_d) and G.

Given a multi index α = (α₁, ..., α_k) ∈ {1, ..., d}^k and for p ≥ 1 we say that IP_α,p(F, G) holds if we may find a random variableHα(F;G)∈L^p such that for every f ∈C_b^∞(R^d) one has

E(∂αf(F)G) =E(f(F)Hα(F;G)). (2.38) The weight Hα(F;G) is not uniquely determined: the one with the lowest variance is E(Hα(F;G) | σ(F)). This quantity is uniquely determined. So we denote

θ_α(F, G) =E(H_α(F;G)|σ(F)). (2.39) Form∈Nandp≥1 we denote byRm,pthe class of random variablesF inR^dsuch that IP_α,p(F,1) holds for every multi indexα with |α| ≤m.We define

T_m,p(F) =kFkp+ X

|α|≤m

kθ_α(F,1)kp. (2.40)

Notice that by H¨older’s inequality kE(Hα(F; 1)|σ(F))kp ≤ kHα(F; 1)kp. It follows that for every choice of the weights H_α(F; 1) one has

Tm,p(F)≤ kFkp+ X

|α|≤m

kHα(F; 1)kp. (2.41)

Theorem 2.12. Letm, l ∈Nandp > d. IfF ∈ Rm+1,pthen the law ofF is absolutely continuous and the density p_F belongs to C^m(R^d). Moreover, suppose that F ∈ Rm+1,2(d+1). There exists a universal constant C (depending on d, l and m only) such that for every multi index α with|α| ≤m

|∂_αp_F(x)| ≤CT_1,2(d+1)^d2−1 (F)T_m+1,2(d+1)(F)(1 +kFkl)(1 +|x|)^−l. (2.42)