stochastic inverse problem with noisy simulator
TRANSCRIPT
HAL Id: hal-00685947https://hal.archives-ouvertes.fr/hal-00685947
Submitted on 6 Apr 2012
HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.
Lāarchive ouverte pluridisciplinaire HAL, estdestinĆ©e au dĆ©pĆ“t et Ć la diffusion de documentsscientifiques de niveau recherche, publiĆ©s ou non,Ć©manant des Ć©tablissements dāenseignement et derecherche franƧais ou Ć©trangers, des laboratoirespublics ou privĆ©s.
Stochastic inverse problem with noisy simulator -Application to aeronautical modelNabil Rachdi, Jean-Claude Fort, Thierry Klein
To cite this version:Nabil Rachdi, Jean-Claude Fort, Thierry Klein. Stochastic inverse problem with noisy simulator -Application to aeronautical model. Annales de la FacultĆ© des Sciences de Toulouse. MathĆ©matiques.,UniversitĆ© Paul Sabatier _ Cellule Mathdoc 2012, vol. 21 (3), p. 593-622. ļæ½10.5802/afst.1346ļæ½. ļæ½hal-00685947ļæ½
STOCHASTIC INVERSE PROBLEM WITH NOISY
SIMULATOR
- APPLICATION TO AERONAUTICAL MODEL -
by
Nabil Rachdi, Jean-Claude Fort & Thierry Klein
Abstract. ļæ½ Inverse problem is a current practice in engineering where the goal is toidentify parameters from observed data through numerical models. These numerical mod-els, also called Simulators, are built to represent the phenomenon making possible theinference. However, such representation can include some part of variability or commonlycalled uncertainty (see [4]), arising from some variables of the model. The phenomenon westudy is the fuel mass needed to link two given countries with a commercial aircraft, wherewe only consider the Cruise phase .From a data base of fuel mass consumptions during the cruise phase, we aim at identifyingthe Speciļæ½c Fuel Consumption (SFC) in a robust way, given the uncertainty of the cruise
speed V and the lift-to-drag ratio F .In this paper, we present an estimation procedure based on Maximum-Likelihood estima-tion, taking into account this uncertainty.
RĆ©sumĆ©. ļæ½ Le problĆØme inverse est une pratique assez courante en ingĆ©nierie, oĆ¹ lebut est de dĆ©terminer les causes d'un certain phĆ©nomĆØne Ć partir d'observations de cedernier. Le phĆ©nomĆØne mis en jeu est reprĆ©sentĆ© par un modĆØle numĆ©rique, dont certainescomposantes peuvent comporter une part de variabilitĆ© (voir [4]). Le phĆ©nomĆØne Ć©tudiĆ© estla masse de fuel nĆ©cessaire pour eļæ½ectuer une liaison ļæ½xĆ©e avec un avion commercial, en neconsidĆ©rant que la phase de CroisiĆØre. Le but Ć©tant, Ć partir de donnĆ©es de masses de fuelconsommĆ©es en croisiĆØre, d'identiļæ½er de maniĆØre robuste la consommation spĆ©ciļæ½que SFCde la motorisation en tenant compte de l'incertitude sur la vitesse de croisiĆØre V et sur laļæ½nesse F de l'avion.Dans cet article, nous proposons une procĆ©dure d'estimation basĆ©e sur une mĆ©thode demaximum de vraisemblance, prenant en compte cette incertitude.
Contents
1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22. General setting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23. Parameter estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64. Numerical study : ļæ½rst approach. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75. On the probabilistic modeling of SFC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106. Theoretical result. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Key words and phrases. ļæ½ Inverse Problem, Uncertainty Analysis, Robust characterization,M-estimation.
We are grateful to Henri Yesilcimen for its contribution to the data generation and for fruitfuldiscussions.
2 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
7. Proof of Theorem 6.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
1. Introduction
One of engineering activities is to model real phenomena. Once a model is built (phys-ical principles, state equations, etc.), some parameters have to be identiļæ½ed and somevariables of the model may present some intrinsic variability. Hence, the identiļæ½cation ofparameters should implicitly take into account the uncertainty of variables of the model.In this paper, we present a likelihood-based method to estimate aeronautic parametersin a Fuel mass model. We use an analytical model that can be viewed as a black-boxsimulator. From a data baseMā,1
fuel, ...,Mā,nfuel giving the mass of fuel consumed for n lines
between two given cities with a speciļæ½c commercial aircraft, we aim at identifying theSpeciļæ½c Fuel Consumption (SFC) which corresponds to a characteristic value of engines.The model we use depends in particular on the cruise speed (V ) and on the lift-to-dragratio (F ). These variables present intrinsic variability: the cruise speed may dependon atmospheric conditions and the lift-to-drag ratio is also subjected to variability po-tentially caused by turbulent phenomena. As a matter of fact, the identiļæ½cation of theparameter SFC should take into account the variability of the cruise speed and thelift-to-drag ratio.In this paper, we propose an algorithm taken from the work of N. Rachdi et al. [7].It allows a characterization of SFC from the observed data Mā,1
fuel, ...,Mā,nfuel and model
simulations when the number of observations n is small.This article is organized as follows. In Section 2 we describe the setting of the problem.In Section 3 we build the algorithm for the inverse problem with a Maximum-Likelihoodbased method. In Section 4 we apply the algorithm given in Section 3. In Section 5 weillustrate the eļæ½ect of modeling conditions, particularly the random modeling on SFCand the number of observed data. In Section 6 we establish the Theorem 6.2 providingan upper bound of the estimation error of the proposed algorithm. Section 7 is devotedto proving the Theorem 6.2.
2. General setting
2.1. Observations. ļæ½ In our study, the data Mā,1fuel, ...,M
ā,nfuel were generated from an
aeronautic software which simulates gas turbine conļæ½gurations used for power generation.In particular, it can simulate the consumed mass of fuel at some conļæ½guration of engines,altitude, speed, atmospheric conditions, etc. (See Figure 1). This software is verycomplex and very much time consuming. In fact only 200 outputs from this software areavailable by choosing various atmospheric conditions. This sample constitutes our datareference.In a ļæ½rst time, we pick up a small sample of size n = 32 from this reference sample.
The data are given in Table 1.Next, we will suppose that the observations Mā,1
fuel, ...,Mā,nfuel are drawn from an un-
known probability distribution Q with associated Lebesgue density f with support
I := [Minf = 7600,Msup = 8100] .
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 3
Figure 1. Aeronautic software
Reference Fuel Masses [kg]
7918 7671 7719 7839 7912 7963 7693 78157872 7679 8013 7935 7794 8045 7671 79857755 7658 7684 7658 7690 7700 7876 77698058 7710 7746 7698 7666 7749 7764 7667
Table 1. Simulated mass of fuel consumptions from aeronautic software
The diļæ½erenceMsupāMinf = 500 kg have to be thought as an overconsumption of about7% approximatively.
2.2. A simpliļæ½ed aeronautical model. ļæ½ We recall that we are interested in iden-tifying the speciļæ½c fuel consumption SFC. It is a signiļæ½cant factor determining thefuel eļæ½ciency of a particular engine. To handle this problem we introduce a classicalsimpliļæ½ed Fuel mass model given by the BrĆ©guet formula:
Mfuel = (Mempty +Mpload)(eSFCĀ·gĀ·Ra
V Ā·F 10ā3 ā 1).(1)
The ļæ½xed variables are
ā¢ Mempty : Empty weight = basic weight of the aircraft (excluding fuel and passengers),ā¢ Mpload : Payload = maximal carrying capacity of the aircraft,ā¢ g : Gravitational constant,ā¢ Ra : Range = distance traveled by the aircraft.
The uncertain variables mentioned in the introduction are
ā¢ V : Cruise speed = aircraft speed between ascent and descent phase,ā¢ F : Lift-to-drag ratio = aerodynamic coeļæ½cient.
Table 2 gives the ļæ½xed variables values and the nominal values considered for uncertainvariables.
2.3. Noise modeling. ļæ½ As said in the introduction, we have to take into accountthe uncertainty of the cruise speed V and the lift-to-drag ratio F . Given the nominalvalue of each variable (see Table 2), an expert judgment can derive the uncertaintybounds. (see Table 3).The uncertainty on the cruise speed V represents a relative diļæ½erence of arrival time
of 8 minutes.
4 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
input value or nominal value unit
Mempty 42600 kgMpload 19900 kgg 9.8 m/s2
Ra 3000 kmVnom 231 m/sFnom 19 ļæ½
Table 2. Values of Fuel mass model inputs
variable nominal value min max
V 231 226 234F 19 18.7 19.05
Table 3. Minimal and maximal values of uncertain variables
Moreover, specialists in turbine engineering propose to model the uncertainties as pre-sented in Table 4.
variable distribution parameter
V Uniform (Vmin, Vmax)F Beta (7, 2, Fmin, Fmax)Table 4. Uncertainty modeling
The probability density function of a beta distribution on [a, b] with shape parameters(Ī±, Ī²) is
g(Ī±,Ī²,a,b)(x) =(xā a)(Ī±ā1)(bā x)Ī²ā1
(bā a)Ī²ā1B(Ī±, Ī²)1 [a,b](x) ,
where B(Ā·, Ā·) is the beta function.Figure 2 shows the probability density functions of V and F .
18.80 18.85 18.90 18.95 19.00 19.05 19.10 19.15
02
46
8
Uncertainty on the liftātoādrag ratio
x
PD
F
Uncertainty on F
(a)
225 230 235
0.00
0.05
0.10
0.15
Uncertainty on the cruise speed
x
PD
F
Uncertainty on V
(b)
Figure 2. (a) Uncertainty on F . (b) Uncertainty on V .
In order to emphasize the "noisy" feature of the variables V and F , we will use thewriting
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 5
ā¢ V = Vnom + ĪµV ,ā¢ F = Fnom + ĪµF ,
where ĪµV is a centered uniform random variable on the interval [ĪµminV , ĪµmaxV ] with
ĪµminV = Vmin ā Vnom and ĪµmaxV = Vmax ā Vnom .
Variable ĪµF , supposed to be independent of ĪµV , is a beta random variable on the interval[ĪµminF , ĪµmaxF ] with shape parameters (7, 2) where
ĪµminF = Fmin ā Fnom and ĪµmaxF = Fmax ā Fnom .
2.4. Robust identiļæ½cation of SFC. ļæ½ In our developments, we will not considerparameter SFC to be deterministic but it will be supposed random. We indeed do notonly need to compute SFC. We also want to take into account its own variability inorder to have a robust characterization of this parameter.
Assumption 2.1. ļæ½ Let us assume that the random variable SFC is compactly sup-ported.
This assumption will allow to apply the theoretical framework (presented in annex)which needs the noise to be compactly supported. Such assumption does not impactthe numerical results.
As a ļæ½rst approach, let us assume that
SFC = ĀµSFC + ĻSFC ĪµSFC , ĪµSFC ā¼ NT (0, 1) .(2)
with unknown parameters ĀµSFC and ĻSFC .We give the following ranges of variation
ĀµSFC ā [15, 20] and ĻSFC ā [s, 1] ,
for a small s > 0 .The distribution NT (0, 1) of ĪµSFC is a symmetric truncated standard Gaussian on theinterval [ā3, 3].
Now, our problem amounts to estimating the location parameter ĀµSFC and the standarddeviation ĻSFC .
Remark 2.2. ļæ½ All the numerical results we will present have been generated withouttruncation, which will not have a signiļæ½cant eļæ½ect. Indeed, the truncations are set tohigh quantiles (more than 99% for upper quantiles and less than 1% for lower quantiles).
2.5. Statistical modeling. ļæ½ Let us denote by (E ,PĪµ) the probability space associ-ated to the noise vector
Īµ = (ĪµSFC , ĪµV , ĪµF )T ,
and denote the vector of parameters by
Īø = (ĀµSFC , ĻSFC)T .
Then, we consider the analytical and simpliļæ½ed model of Mfuel the mass of consumedfuel, as the function
Mfuel = h(Īµ,Īø) ,
6 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
where h : (E ,PĪµ)ĆĪā Ih is given by
h(Īµ,Īø) = (Mempty +Mpload)
(exp
((ĀµSFC + ĻSFC ĪµSFC) Ā· g Ā·Ra(Vnom + ĪµV ) Ā· (Fnom + ĪµF )
Ā· 10ā3
)ā 1
)(3)
with
Ī = [15, 20]Ć [s, 1]
and Ih is the interval
Ih = h(E ,Ī) = [Mhinf ,M
hsup] .(4)
We denote by |Ih| its length.
Remark 2.3. ļæ½ We observe through simulations that
I ā Ih ,
where I = [Minf ,Msup] is the observation interval given above.
The purpose is now to estimate parameter Īø ā Ī from the set of data Mā,1fuel, ...,M
ā,nfuel.
In the next section, we propose an estimation procedure taken from [7].
3. Parameter estimation
In our previous framework it is possible to apply the procedures developed in [7].In particular, we choose to work with the logācontrast which can be understood as aMaximum Likelihood based estimation.
The sample Mā,1fuel, ...,M
ā,nfuel is drawn from an unknown distribution Q. We will use the
parametric family of distributions {QĪø , Īø ā Ī} where QĪø is the pushforward measure ofPĪµ by the (measurable) application u 7ā h(u,Īø) .That means that we consider the models h(Īµ, Īø) to be a reasonable ļæ½rst approximationin order to obtain statistical information about Q.Denoting ĻĪø the Lebesgue density associated to the measure QĪø, the maximum likelihoodprocedure is given by
Īø = ArgminĪøāĪ
ā 1
n
nāi=1
log(ĻĪø(Mā,ifuel)) .(5)
However, the above procedure is unfeasible because the density ĻĪø is not analyticallytractable. As suggested in [7], we replace ĻĪø by an estimator denoted ĻmĪø . There aremany ways to estimate a density. We choose the simplest one, which is commonly usedin industrial modeling.
Let Īµ1, ..., Īµm be m random variables i.i.d from PĪµ, we consider the kernel estimate of ourdensity
ĻmĪø (Ā·) =1
m
māj=1
KbmĪø(Ā· ā h(Īµj,Īø)) ,(6)
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 7
where KbmĪøis the Gaussian kernel
KbmĪø(x) =
1ā2Ļ bmĪø
eā x2
2 (bmĪø
)2 ,
and bmĪø is computed from the sample h(Īµj,Īø), j = 1, ...,m for Īø ā Ī, by Silverman'srule-of-thumb :
bmĪø =
(4
3
)1/5
mā1/5 ĻĪø .(7)
The quantity ĻĪø is the empirical standard deviation of the sample h(Īµj,Īø), j = 1, ...,m
ĻĪø =1
m
māj=1
(h(Īµj,Īø)ā 1
m
māj=1
h(Īµj,Īø)
)2
.
Other popular estimates are truncated projections on a suitable basis of functions(sine, cosine, wavelets, etc.). Here we do not discuss the optimization of the densityestimation, but all what follows could be applied in the same way. The numerical resultsmay be slightly diļæ½erent, but qualitatively the same.Replacing ĻĪø by ĻmĪø in (5) and simplifying by the multiplying constant 1/n yields theestimation procedure
Īø = ArgminĪøāĪ
ānāi=1
log
(1
m
māj=1
KbmĪø
(h(Īµj,Īø)āMā,i
fuel
)).(8)
Hence, our problem is an inverse problem. More precisely, it is an inverse problem inpresence of uncertainties, also called probabilistic inverse problem or stochastic inverseproblem. This topic is often treated in the ļæ½eld of uncertainty management: the goalmay for instance be to identify the intrinsic uncertainty of a system, see for instancethe PhD works [1] and [5]. Another reference is the paper of E. de Rocquigny and S.Cambier [3], where the purpose is to identify a parameter of interest which controlsthe vibration ampliļæ½cation of stream turbines. Our framework is diļæ½erent. The maindiļæ½erence lies in the absence of assumptions, in the present paper, on the distributionof the error between observation data and reference data. Thus, it diļæ½erentiates theestimation procedures we propose from the ones developed in [3].
In the following section, we provide a numerical analysis using the algorithm given by(8). Theoretical aspects will be addressed in Section 6.
4. Numerical study : ļæ½rst approach
4.1. Estimation. ļæ½ Setting
J(Īø) = ānāi=1
log
(1
m
māj=1
KbmĪø
(h(Īµj,Īø)āMā,i
fuel
))with Īø = (ĀµSFC , ĻSFC)T ,
our problem is a minimization problem where we want to compute
Īø = ArgminĪøāĪ
J(Īø) .
8 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
We recall that n = 32. The data (Mā,ifuel)i=1,...,n are provided by Table 1. We choose
m = 10000 (the number of calls to the model given by (3)), and for j = 1, ...,m, Īµj ā¼ PĪµ
where
PĪµ(du, dv, dw) =1ā
2Ļ Leāu
2/2 g(7,2,ĪµminF ,ĪµmaxF )(v)1
ĪµmaxV ā ĪµminV
1 [ĪµminV ,ĪµmaxV ](w) du dv dw ,
with L = Ī¦(3)āĪ¦(ā3) (Ī¦ is the cumulative distribution function of a standard Gaussianrandom variable).This optimization procedure can be solved by Quasi-Newton methods. We present theresults in Table 5. In Figure 3 we show the resulting probability density function of SFC
estimator value of J(Īø) estimated SFC location estimated SFC dispersion
Īø J(Īø) = 199.465 ĀµSFC = 17.397 ĻSFC = 0.201Table 5. SFC characterization parameters
given by NT (ĀµSFC , ĻSFC) .
Figure 3. Estimated Speciļæ½c Fuel Consumption distribution.
Figure 4 provides proļæ½le views of the criterion function Īø = (ĀµSFC , ĻSFC) 7ā J(Īø),ļæ½rst at ĻSFC = ĻSFC (Figure 4(a), we show log(J(Īø))) and then at ĀµSFC = ĀµSFC (Figure4(b)).
We notice that the minimum Īø = (ĀµSFC , ĻSFC) is correctly located.
4.2. Comparison with reference sample. ļæ½ In order to analyse the results ob-tained in the previous subsection, we need some reference sample of SFC values at thesame simulation conditions. We take for reference the sample of SFC values of size 200described in the introduction, provided by the aeronautic software. The characteristicsof this sample are given in Table 6.
Mean Stand. dev.
Reference sample 17.49 0.57Table 6. Reference sample characteristics
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 9
(a) (b)
Figure 4. (a) Proļæ½le view of log(J) at ĻSFC = ĻSFC . (b) Proļæ½le view of J atĀµSFC = ĀµSFC .
The data in Table 6 have to be compared with those in Table 5 where the mean andstandard deviation are ĀµSFC = 17.397 and ĻSFC = 0.201, respectively. Table 7 providesthe associated relative errors. Figure 5 shows the histogram of the reference sample andthe estimated distribution of SFC obtained in Figure 3.
Reference sample Estimated SFC (3) Relative error
Mean 17.49 17.397 0.5 %Stand. dev. 0.57 0.201 60.6 %
Table 7. Relative errors of the mean and deviation between reference SFCsample and the estimated model (3)
It appears that the location of the variable of interest SFC is well reached whereas thestandard deviation estimation provides an error of 60%. The "error" has roughly twoorigins:
Statistical error : this error is mainly due to the limited number (n = 32) of datafrom the observed masses of fuel Mā,i
fuel. It is also due to the error induced by thekernel approximation of ĻĪø.Yet the choice ofm = 10000 calls to the analytical modelgarantees that the error on ĻĪø is small .
Model error : this error is relative to the use of Fuel mass model (1) with uncertainvariables V and F (Figure 2), and includes the Gaussian hypothesis for SFC (2).Thus, the model error can be separated into 2 parts: physical model error anduncertainty modeling error.
We observe on Figure 5 that the SFC parameter does not behave like a Gaussianvariable. This can be qualiļæ½ed as model error.However, if one just wants to estimate the mean value of SFC, the Gaussian hypothesisdoes not have a signiļæ½cative impact (0.5% of error). On the other hand, if one wantsmore information about SFC, other modeling tools are needed to allow a robust
10 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
Figure 5. Reference and estimated SFC distributions.
characterization approach.
In the next subsection, we will discuss the uncertainty modeling, more precisely, theGaussian hypothesis for SFC given by (2).
5. On the probabilistic modeling of SFC
5.1. Considering Wiener-Hermite representation in the previous analysis. ļæ½The characterization of a random variable by the mean and the standard deviation onlycould be too approximative in order to study the whole behavior of the variable. In thisstudy, we have made an a priori (a model) on the variable of interest SFC. In (2) wesupposed that
SFC ā¼ NT (ĀµSFC , Ļ2SFC) ,
which we now rewrite
SFC = ĀµSFC + ĻSFC Ī¾T , Ī¾T ā¼ NT (0, 1) .(9)
We will see that this (truncated) Gaussian hypothesis on SFC is a particular case of amore general representation.
The so called Wiener Chaos Expansion, developed in the 30's by Wiener [11], gives arepresentation of any second-order random variable Z:
Z =āāl=0
zlĪ„l((Ī¾k)kā„1) , (with convergence in L2(P) )(10)
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 11
where (Ī¾k)kā„1 is a (inļæ½nite) sequence of independent standard normal random variablesand the Ī„l's are the multivariate Hermite polynomials. This expansion is also calledWiener-Hermite expansion.In practice, we have to consider a ļæ½nite sequence (Ī¾1, ..., Ī¾M) where M is called theorder of the expansion, and the sum in (10) is truncated at p which is the degree of theexpansion.Hence, considering all M -dimensional Hermite polynomials of degree lower than p, therepresentation (10) is approximated by
Z ' Zp,M =Pā1āl=0
zlĪ„l(Ī¾) , Ī¾ = (Ī¾1, ..., Ī¾M) ,(11)
where
P =(M + p)!
M ! p!.
The integer P corresponds to the number of coeļæ½cients to be estimated.For our purpose, by Assumption 2.1, we will consider the following approximation
Z ' Zp,M =Pā1āl=0
zlĪ„l(Ī¾T ) , Ī¾T = (Ī¾1T , ..., Ī¾
MT ) ,(12)
where Ī¾T is a vector of truncated standard Gaussian variables.Moreover, one can notice that by orthogonality arguments in (12), we have withouttruncation
E(Zp,M) = z0 = E(Z)(13)
and
Var(Zp,M) =Pā1āl=1
z2l .(14)
Let us notice that by the decomposition
Zp,M = Z +(Zp,M ā Z
),
each choice of p and M will induce a model error
moderr := Zp,M ā Z .We illustrate this aspect concerning SFC in the next subsection.
5.2. Application to the Speciļæ½c Fuel Consumption. ļæ½ In our purpose, if we sup-pose that E(SFC2) <ā (it is implicitly supposed in the Gaussian hypothesis), we canset the following modeling
SFCp,M =Pā1āl=0
zlĪ„l(Ī¾T ) , Ī¾T = (Ī¾1T , ..., Ī¾
MT ) , M, p ā„ 1
which we rewrite by (13)
SFCp,M = ĀµSFC +Pā1āl=1
zlĪ„l(Ī¾T ) , Ī¾T = (Ī¾1T , ..., Ī¾
MT ) , M, p ā„ 1 .(15)
It appears now that the Gaussian representation (9) is the particular case of (15) withp = 1 andM = 1. Moreover, in view of the Wiener representation (10), the Gaussian one
12 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
(9) may lead to a rough approximation (if SFC is not Gaussian) and thus contributesto a non negligible model error. It is clearly observed in Figure 5 where the referencedata does not seem to be drawn from a Gaussian distribution.As a matter of fact, one can hope to reduce the model error (described in the previoussubsection), at least the error corresponding to SFC modeling, by considering a lessrestrictive representation (15) with some appropriate order M ā„ 1 and degree p ā„ 1 .Let us consider the Wiener-Hermite expansion of order M = 2 and degree p = 2
SFC2,2 = ĀµSFC +5āl=1
ĪølĪ„l(Ī¾T ) , Ī¾T = (Ī¾1T , Ī¾
2T )
or
SFC2,2 = ĀµSFC + Īø1Ī¾1T + Īø2Ī¾
2T + Īø3Ī¾
1T Ī¾
2T + Īø4((Ī¾1
T )2 ā 1) + Īø5((Ī¾2T )2 ā 1) ,(16)
that leads to estimate P = (2+2)!2!2!
= 6 coeļæ½cients. Table 8 shows the result obtained bythe algorithm developed in the previous section where we change the function h(Īµ,Īø) in(3) replacing ĻSFCĪµSFC by Īø1Ī¾
1T + Īø2Ī¾
2T + Īø3Ī¾
1T Ī¾
2T + Īø4((Ī¾1
T )2 ā 1) + Īø5((Ī¾2T )2 ā 1) , with
Īµ = (Ī¾1T , Ī¾
2T , ĪµV , ĪµF ) and Īø = (ĀµSFC , Īø1, ..., Īø5) .
Īø0 Īø1 Īø2 Īø3 Īø4 Īø5
SFC2,2 17.470 0.047 0.054 0.182 0.103 0.063Table 8. SFC characterization parameters
Reference sample from SFC2,2 Relative error
Mean 17.49 17.470 0.11 %Stand. dev. 0.57 0.230 59.65 %
Table 9. Relative errors of the mean and standard deviation with SFC2,2
Let us compare the relative errors of the ļæ½rst two statistical moments by consideringSFC1,1 (i.e the Gaussian hypothesis Table 7) and SFC2,2 (Table 9).The Wiener-Hermite modeling seems to improve the mean estimation of SFC whereasthe standard deviation is poorly estimated in the two cases with an error of about60%. There is no signiļæ½cative diļæ½erence between the two methods regarding the ļæ½rsttwo moments. However, the behavior of density functions corresponding to SFC1,1 (seeFigure 5) and SFC2,2 is clearly not the same. We present in Figure 6 the result obtainedwhen SFC is modeled by a Wiener expansion of order M = 2 and degree p = 2.The distribution of SFC given by the Wiener expansion in Figure 6 seems to have
a behavior close to the reference sample one, despite the fact that there is a nonnegligible bias. As mentioned in the previous subsection, this is due to the statisticaland model errors. Indeed, let us recall that we have at disposal n = 32 reference fuelmasses from which we characterize the SFC parameter. It would be interesting tosee what happens when adding reference fuel masses, i.e by reducing the statistical error.
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 13
Figure 6. Estimations of SFC probability density with a Wiener Expansionp =M = 2 .
5.3. Wiener-Hermite analysis with augmented reference fuel mass sample.ļæ½ We present here numerical results obtained by adding 50 new samples from the databasis built with the complex software with the same initial conditions. Figure 7 showsthe characterization of SFC obtained by a Wiener expansion of orderM = 2 and degreep = 2 from the augmented reference fuel mass sample of size n = 82.The Table 10 gives the coeļæ½cients corresponding to this simulation.
Īø0 Īø1 Īø2 Īø3 Īø4 Īø5
SFC2,2 17.50 0.281 0.008 0.012 0.191 0.219Table 10. SFC characterization parameters from augmented fuel mass sample
Reference sample from SFC2,2 Relative error
Mean 17.49 17.50 0.06 %Stand. dev. 0.57 0.404 29.12 %
Table 11. Relative errors of the mean and standard deviation with SFC2,2
from augmented fuel mass sample
Hence, by adding reference data we improve signiļæ½catively the characterization ofSFC on the ļæ½rst two statistical moments as well as on the whole probability densityfunction of SFC.
14 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
Figure 7. Characterization of SFC with an augmented sample of fuel mass
In the next section we introduce some knowledge on the SFC modeling through anexpert judgment inducing a new and statistical modeling that will improve to be better.
5.4. Analysis with a "good" a priori knowledge. ļæ½ In the previous analyses, weonly consider truncated Wiener-Hermite expansions. This is more of a mathematicalhypothesis than a knowledge brought to the modeling. Suppose now that an expertjudgment says that the distribution of the SFC is of exponential form. Mathematically,it is equivalent to supposing that the probability density of SFC belongs to the family{
p(u;Īø) = Īø2 eāĪø2(uāĪø1) 1 [Īø1,+ā[ , Īø = (Īø1, Īø2) ā R+ Ć Rā+
}.
One can check that this suggestion induces the modeling
SFCexp = Īø1 ā1
Īø2
log(Ī¾) , Ī¾ ā¼ U([0, 1]) ,(17)
where U([0, 1]) is the uniform distribution on the interval [0, 1] .In order to satisfy Assumption 2.1, we consider the truncated version
SFCexp = Īø1 ā1
Īø2
log(Ī¾T ) , Ī¾T ā¼ U([c, 1]) ,(18)
for some small c > 0.
Remark 5.1. ļæ½ Representation (18) seems quite diļæ½erent from the one provided bythe Wiener-Hermite expansions (see (9) and (16)). Yet, as the random variable SFCexp
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 15
has ļæ½nite variance, the modeling (18) could be seen as a practical alternative to a Wienerexpansion (10). Such a Wiener expansion would be given by choosing a high order M exp
and a high degree pexp in (12).
In what follows, we present the results of the numerical analysis corresponding ton = 32 and n = 82.
Īø1 Īø2
SFCexp 17.23 3.45Table 12. Estimation of Īø = (Īø1, Īø2) when n = 32
Reference sample from SFCexp (n = 32) Relative error
Mean 17.49 17.52 0.17 %Stand. dev. 0.57 0.29 49.12 %
Table 13. Relative errors of the mean and standard deviation between referenceSFC sample and SFCexp when n = 32.
Īø1 Īø2
SFCexp 16.95 2Table 14. Estimation of Īø = (Īø1, Īø2) when n = 82
Reference sample from SFCexp (n = 82) Relative error
Mean 17.49 17.45 0.23 %Stand. dev. 0.57 0.501 12.1 %
Table 15. Relative errors of the mean and standard deviation between referenceSFC sample and SFCexp when n = 82.
We clearly see that the informative knowledge contributes to improving signiļæ½cativelythe characterization of the Speciļæ½c Fuel Consumption. With n = 82 fuel mass data, theresults are satisfying as shown in Figure 8 and Table 15.
5.5. Conclusion. ļæ½ Section 5 was dedicated to illustrating the eļæ½ect of the modelingconditions for SFC characterization. In particular, we showed the impact of a "modelerror" through the modeling of the random variable SFC. We also illustrated how thestatistical error, through the number of fuel mass data, appears in the performance ofthe estimation.In all cases, we computed a parameter Īø. If we supposed that there is no model error,that is Q ā {QĪø, Īø ā Ī} (where Q is the "true" distribution of Mā
fuel), the error isonly due to the limited number of data. So it makes sense to investigate the diļæ½erence
āĪø ā Īøāā, where Īøā is deļæ½ned as Q = QĪøā . If this is not the case it gives an insight onthe statistical error part.It is the topic of the following section.
16 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
Figure 8. Characterization of SFC with an exponential hypothesis
6. Theoretical result
In this paper, the study of the procedure performance (8) will be non-asymptotic, i.efor a ļæ½xed number of observations Mā,i
fuel (n) and a ļæ½xed number of variables Īµj (m).The asymptotic study is let to a forthcoming work.
The quality of such estimation procedure can be investigated by giving an upper bound
of the distance between the reachable parameter Īø and the best parameter Īøā (unknown).The latter can be seen as the parameter obtained if one has an inļæ½nite number of obser-vations Mā,i
fuel and variables Īµj. More precisely,
Īøā = ArgminĪøāĪ
EQ log(ĻĪø(Mā
fuel)),(19)
where EQ log(ĻĪø(Mā
fuel))can be seen as the "limit" of the quantity
1
n
nāi=1
log
(1
m
māj=1
KbmĪø
(h(Īµj,Īø)āMā,i
fuel
))(20)
in (8) when n and m go to inļæ½nity.The Maximum Likelihood equation (19) turns out to be the minimization of theKullback-Leibler divergence between Q and the family {QĪø, Īø ā Ī}, while equation (20)is a smoothed empirical counterpart.
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 17
We consider the model h(Īµ,Īø) given in (3), but what follows can be generalized to anyother one.Then, denoting by ā Ā· ā the Euclidian norm in R2, it makes sense to bound the quantity
āĪø ā Īøāā2 .
Let us denote byR(Īø) := EQ log
(ĻĪø(Mā
fuel)),
and by f the Lebesgue density associated to the measure Q .
Assumptions 6.1. ļæ½ Let us consider the following assumptions.- A1 The map Īø 7ā R(Īø) is twice diļæ½erentiable with
āR(Īøā) = 0
and has a symmetric positive deļæ½nite Hessian matrix ā2R . Let us denote by Ī»min >0 the smallest eigenvalue of the set of matrices {ā2R(Īø), Īø ā Ī} .
- A2 It exists Ī· > 0 such that for all Īø ā Ī, the density probability of h(Īµ,Īø) wenoted ĻĪø, satisļæ½es
ĻĪø > Ī· .
- A3 For all Īø ā Ī, the second derivative of ĻĪø, we note Ļā²ā²
Īø, exists and
C := supĪøāĪāĻā²ā²Īøā2 < +ā .
- A4 We suppose that
0 < Ī“ < infĪøāĪ
ĻĪø and supĪøāĪ
ĻĪø < Ļ < +ā ,
where ĻĪø is deļæ½ned in (7).
We prove the following consistency theorem:
Theorem 6.2. ļæ½ Let us consider the estimator Īø in (8) and the Assumptions (6.1).Then, for all 0 < Ļ < 1/2, with probability at least 1ā 2 Ļ
āĪø ā Īøāā2 ā¤ c1
ālog(a1Ļā1)
n+c2
ālog(a2Ļā1) + c3m
1/10
ām
,
for some constants c1, c2, c3, a1 and a2 .
The risk bound of this theorem seems surprising since we obtain a rate of n1/4, whereasone expects a rate close to
ān for the treated parametric problem. This theorem is a
consistency result. Consequently, it does not give information about the rate of conver-gence. The obtained bound can be explained by the fact that, by Assumption A1, wehave
R(Īø)āR(Īøā) ā āĪø ā Īøāā2 .
Indeed, if R(Īø)āR(Īøā) ā 1/ān (bound given in [7]) then obviously āĪøā Īøāā ā nā1/4 .
However, theān-rate can be reached by considering the approach of Corollary 5.53 (pp.
77) in [9] where an additional assumption is made on the risk function Īø 7ā R(Īø). Moreprecisely, this assumption relies on the function Īø 7ā log(ĻĪø) which is supposed to satisfya Lipschitz condition. This work is let to a forthcoming paper which will deal with
a central limit theorem for the parameter Īø (the rate of convergence will therefore bereachable).
18 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
7. Proof of Theorem 6.2
We give a general proof of Theorem 6.2.
By Assumption A1, we have the Taylor-Lagrange formula
R(Īø) = R(Īøā) +1
2(Īø ā Īøā)T ā2R(Ī¾) (Īø ā Īøā) ,(21)
for some Ī¾ ā Ī .Then, we will use the following lemma
Lemma 7.1 (Rayleigh's quotient). ļæ½ Let H be a real symmetric matrix p Ć p anddenote by Ī»1 < ... < Ī»p the ordered eigenvalues of H.It holds that for all x ā Rp ā {0}
Ī»1 ā¤xT H x
xT xā¤ Ī»p .
Now, applying this lemma with H = ā2R(Ī¾) and x = (Īø ā Īøā) yields
Ī»min āĪø ā Īøāā2 ā¤ (Īø ā Īøā)T ā2R(Ī¾) (Īø ā Īøā) ,
where Ī»min > 0 is the smallest eigenvalue of the set of matrices {ā2R(Īø), Īø ā Ī} .Then, using this last inequality with equality (21) gives
āĪø ā Īøāā2 ā¤ 2
Ī»min
(R(Īø)āR(Īøā)
).(22)
The problem turns to bound the positive quantity R(Īø)āR(Īøā), where Īø is given by(8). Such bound can be investigated by applying Theorem 4.1 in [7], which is a generalresult. We will aim at computing constants KĻ
1 and KĻ2 such that, with high probability
(23)
R(Īø)āR(Īøā) ā¤ 2 āfā2
Ī·
(1ān
Ī·
2 Ī“2 āfā2
Ī³ KĻ1 +
1ām
1ā2Ļ Ī“
KĻ2 +
1
m2/5
C (1.06Ļ)2
ā3
).
In our framework, the main work is to compute the concentration constants KĻ1 and KĻ
2
derived from [7] in the following particular case.
7.1. On concentration constants KĻ1 and KĻ
2 . ļæ½ Since we apply Theorem 4.1 in[7], one has to guarantee that the constants KĻ
1 and KĻ2 are ļæ½nite.
For this, let us ļæ½rst recall some deļæ½nitions and notations relative to empirical processes.
Deļæ½nition 7.2. ļæ½ Empirical process. Let W be some probability measure on somespace T and let us suppose given a k i.i.d sample Ī¾1, ..., Ī¾k drawn from W . Let us denoteby Wk the empirical measure
Wk :=1
k
kāi=1
Ī“Ī¾i
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 19
and G some class of real valued functions g : T ā R .We call W -empirical process indexed by G the following application
Gk : G āā R
g 7āā Gk :=āk
ā«T
g(t) (Wk āW ) (dt) ,
also written
Gk g :=1āk
kāi=1
(g(Ī¾k)ā EW (g(Ī¾))) .
We denote the supremum of an empirical process by
āGkāG := supgāG|Gk g| .
Following the proof lines of Theorem 2.1, Table 2 p.11 in [7] (giving classes of functions)and considering the inequality (23), it is easy to verify that KĻ
1 is deļæ½ned as
for all n ā„ 1 , P(āUnāA ā¤ KĻ1 ) ā„ 1ā Ļ(24)
where Un is the Q-empirical process (Qn = 1n
āni=1 Ī“Mā,ifuel
) indexed by the class of func-
tions
A = {y ā I 7āā (y ā Ī»)2, Ī» ā Ih}(25)
where we recall
I = [Minf ,Msup] and Ih = h(E ,Ī) .
Similarly, the constant KĻ2 is deļæ½ned as follows
for all m ā„ 1 , P(āVmāB ā¤ KĻ2 ) ā„ 1ā Ļ(26)
where Vm is the P Īµ-empirical process (P Īµm = 1
m
āmj=1 Ī“Īµj) indexed by the class of functions
B = {x ā E 7āā eā(h(x,Īø)āĪ»)2/2 b2 , (Īø, Ī», b) ā ĪĆ Ih Ć [Ī“, Ļ]} .(27)
By the writings (24) and (26), the constants KĻ1 and KĻ
2 arise from the "concentration ofthe measure phenomenon" (see [6], [2]). More precisely, these constants characterize thetightness of the sequences of random variables āUnāA (which is (Mā,i
fuel)i=1,...,n dependent)and āVmāB (which is (Īµj)j=1,...,m dependent).
Now, we aim at computing (upper bound) these constants using concentration inequali-ties where the classes of functions A and B will play a crucial role. In particular, we willapply the following theorem which is Theorem 2.14.9 in [10].Before, let us recall the deļæ½nition of the bracketing numbers (taken from [10] p. 83-85).
Deļæ½nition 7.3. ļæ½ Bracketing numbers. Let G be some class of functions on T anddenote by W a probability measure on T .Given two functions l, u, the bracket [l, u] is the set of all functions g with l ā¤ g ā¤ u. AnĪµ-bracket is a bracket [l, u] with ||uāl||2,W < Īµ. The bracketing number N[ ](Īµ, G, L2(W ))is the minimum number of Īµ-brackets needed to cover the class of functions G.The entropy with bracketing is the logarithm of the bracketing number.
Remark 7.4. ļæ½ The bracketing numbers measure the "size", the complexity of a classof functions.
20 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
Theorem 7.5. ļæ½ Let G be a uniformly bounded class of (measurable) functions g :T ā [0, 1] and denote by W a probability measure on T . If the class G satisļæ½es, for someconstants K and L
N[ ](Īµ,G, L2(W )) ā¤(K
Īµ
)Lfor every 0 < Īµ < K .(28)
Then, for every t > 0,
P(āGkāG > t) ā¤(D tāL
)Leā2t2 ,
for a constant D that only depends on K.
The proof of this theorem can be found in [8].
Now, let KĻ be a constant (to determine) which satisļæ½es
P(āGkāG ā¤ KĻ ) ā„ 1ā Ļ .This is equivalent to
P(āGkāG > KĻ ) ā¤ Ļ .(29)
By Theorem 7.5, applied with t = KĻ , we have
P(āGkāG > KĻ ) ā¤(DKĻ
āL
)Leā2(KĻ )2
.(30)
Hence, the constant KĻ can be taken such that(DKĻ
āL
)Leā2(KĻ )2 ā¤ Ļ ,
which is similar to
(KĻ )2 ā L
2log(KĻ ) ā„ log(aL,D Ļ
ā1)
2, with aL,D =
(DāL
)L.(31)
Then, for small enough Ļ > 0, let us consider the constant
KĻ =
ālog(aL,D Ļā1)
2(32)
which satisļæ½es (31).
Finally, we see that the constant KĻ can be characterized (only) by the class of functionsG through the constants D and L provided by Theorem 7.5.
In our purpose, the classes of interest areA and B deļæ½ned in (25) and (27), respectively.Next, one can easily check that these classes are uniformly bounded and it is suitable towork with normalized classes
A = Ī±A +1
Ī²AA ,(33)
B = Ī±B +1
Ī²BB ,(34)
such that all the functions take values in [0, 1] .
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 21
Now, we have to prove that the classes A and B have polynomial bracketing numbersfollowing (28). This will give the constants LA, DA and LB, DB needed to identify thekey constants KĻ
1 and KĻ2 deļæ½ned in (24) and (26), respectively.
7.2. Characterization of LA, DA, LB, DB. ļæ½ We consider the Theorem 2.7.11 in[10] (p. 164) which deals with classes that are Lipschitz in a parameter. It reads:
Theorem 7.6. ļæ½ Let G = {t ā T 7ā gs(t) , s ā S} be a class of functions satisfying
for all t ā T , s, sā² ā S , |gs(t)ā gsā²(t)| ā¤ d(s, sā²)G(t) ,
for some metric d on S and some function G : t 7ā G(t).Then, for any norm
N[ ](2Īµ āGā,G, ā Ā· ā) ā¤ N(Īµ, S, d) ,
where N(Īµ, S, d) is the minimal number of balls {r , d(r, s) < Īµ} of radius Īµ needed tocover the set S .
In what follows, we detail the case of the class A. The case of the class B is exactlyin the same spirit.
Let us recall that Q is the probability measure considered on I (observation space) andthat we have
A = {fĪ» : y ā I 7āā Ī±B +1
Ī²A(y ā Ī»)2, Ī» ā Ih} ,
where I = [Minf ,Msup] and Ih = [Mhinf ,M
hsup] (with I ā Ih).
So
|fĪ»(y)ā fĪ»(y)| = 1
Ī²A|(y ā Ī»1)2 ā (y ā Ī»2)2| ā¤ |Ī»1 ā Ī»2|F (y) ,
with F (y) = 2Ī²A
(y+Mhsup), and by Theorem 7.6 applied with ā Ā· ā = ā Ā· ā2,Q, it holds that
N[ ](Īµ, A, L2(Q)) ā¤ N
(Īµ
2 āFā2,Q
, Ih, | Ā· |).
Moreover, sinceāFā2,Q ā¤ sup
yāIF (y) āfā2
where f is the density associated to the measure Q, and using the fact that I ā Ih, weobtain that
āFā2,Q ā¤4
Ī²AMh
sup āfā2 .
This last inequality yields
N
(Īµ
2 āFā2,Q
, Ih, | Ā· |)ā¤ N
(Ī²A Īµ
8Mhsup āfā2
, Ih, | Ā· |).
Since Ih = [Mhinf ,M
hsup], the quantity (covering number) in the right member is bounded
by8 |Ih|Mh
sup āfā2
Ī²A Īµ, |Ih| = Mh
sup āMhinf .
We ļæ½nally get
N[ ](Īµ, A, L2(Q)) ā¤8 |Ih|Mh
sup āfā2
Ī²A Īµ,
22 NABIL RACHDI, JEAN-CLAUDE FORT & THIERRY KLEIN
that is
N[ ](Īµ, A, L2(Q)) ā¤(KAĪµ
)LA,
with
LA = 1
and
KA =8 |Ih|Mh
sup āfā2
Ī²Athat determines DA by [8] .
A similar work gives the constant LB = 1 and a constant DB .
7.3. End of the proof. ļæ½ By the previous subsection, we get the constants KĻA and
KĻB given by (32) with associated constants L and D:
KĻA =
ālog(a1 Ļā1)
2, a1 = aLA,DA = DA(35)
KĻB =
ālog(a2 Ļā1)
2, a2 = aLB,DB = DB(36)
where initially aL,D =(
DāL
)L(by (31)).
But, the constants of interest KĻ1 and KĻ
2 deļæ½ned in (24) and (26) are relative to nonnormalized classes A and B. Let us remark that if G = Ī± + 1
Ī²G, then
āGkāG =1
Ī²āGkāG .(37)
Now, let us denote by KĻG the constant that satisļæ½es
P(āGkāG ā¤ KĻG) ā„ 1ā Ļ ,
and denote by KĻG the constant that satisļæ½es
P(āGkāG ā¤ KĻG) ā„ 1ā Ļ .
By (37), it is easy to check that we can take
KĻG = Ī² KĻ
G .
We deduce that
KĻ1 = Ī²AK
ĻA
and
KĻ2 = Ī²BK
ĻB .
Finally, by (22) and (23) we have with probability 1ā 2Ļ
āĪø ā Īøāā2 ā¤ 4 āfā2
Ī»min Ī·
(1ān
Ī·
2 Ī“2 āfā2
Ī³ KĻ1 +
1ām
1ā2 Ļ Ī“
KĻ2 +
1
m2/5
C (1.06Ļ)2
ā3
)which we rewrite
āĪø ā Īøāā2 ā¤ā
2 c1ānKĻA +
ā2 c2āmKĻB +
c3
m1/5
with corresponding constants c1, c2 and c3 and KĻA, K
ĻB are given by (35) and (36).
That concludes the proof of Theorem 6.2.
STOCHASTIC INVERSE PROBLEM IN AERONAUTICS 23
References
[1] P. Barbillon. (phd thesis) mĆ©thodes d'interpolation Ć noyaux pour l'approximation defonctions type boĆ®te noire coĆ»teuses. 2010.
[2] P. Billingsley. Convergence of probability measures. Wiley New York, 1968.
[3] E. De Rocquigny and S. Cambier. Inverse probabilistic modelling of the sources of un-certainty: a non-parametric simulated-likelihood method with application to an industrialturbine vibration assessment. Inverse Problems in Science and Engineering, 17(7):937ļæ½959,2009.
[4] E. de Rocquigny, N. Devictor, and S. Tarantola, editors. Uncertainty in industrial practice.John Wiley.
[5] E. Kuhn. (phd thesis) estimation par maximum de vraisemblance dans des problemesinverses non linƩaires. 2003.
[6] M. Ledoux. The concentration of measure phenomenon. AMS, 2001.
[7] N. Rachdi, J.C. Fort, and T. Klein. Risk bounds for new M-estimation problems . hal00537236, 2010.
[8] M. Talagrand. Sharper bounds for Gaussian and empirical processes. The Annals of Prob-ability, 22(1):28ļæ½76, 1994.
[9] A.W. van der Vaart. Asymptotic statistics. Cambridge University Press, 2000.
[10] A.W. van der Vaart and J.A. Wellner.Weak Convergence and Empirical Processes. SpringerSeries in Statistics, 1996.
[11] N. Wiener. The homogeneous chaos. American Journal of Mathematics, 60(4):897ļæ½936,1938.
Nabil Rachdi, Institut de MathĆ©matiques de Toulouse - EADS Innovation Works, 12 rue Pasteur,92152 Suresnes ā¢ E-mail : [email protected]
Jean-Claude Fort, MAP5, UniversitĆ© Paris Descartes, 45 rue des saints pĆØres, 75006 ParisE-mail : [email protected]
Thierry Klein, Institut de MathƩmatiques de Toulouse, 118 route de Narbonne F-31062 ToulouseE-mail : [email protected]