TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS

Size: px
Start display at page:

Download "TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS"

Transcription

1 Statistica Sinica 71997, 97-9 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS A. Budhiraja and G. Kallianpur University of North Carolina, Chapel Hill Abstract: Formulae connecting the multiple Stratonovich integrals with single Ogawa and Stratonovich integrals are derived. Multiple Riemann-Stieltjes integrals with respect to certain smooth approximations of the Wiener process are considered and it is shown that these integrals converge to multiple Stratonovich integrals as the approximation converges to the Wiener process. Key words and phrases: Multiple Stratonovich integrals, multiple Wiener integrals, Wong-Zakai approximations. 1. Introduction In an important work Hu and Meyer 1987 introduced a new multiple stochastic integral with respect to a Wiener process, called the multiple Startonovich integral MSI, which is in general different from the usually studied multiple Wiener-Itô integral. However, Hu and Meyer only offered some rather informal definitions and proofs. Johnson and Kallianpur 1993 gave a rigorous definition for the MSI the term MSI was not used in their work, denoted in this work by δ p, and gave necessary and sufficient conditions for its existence. Recently, multiple Stratonovich integrals have been applied to problems in asymptotic statistics and nonlinear filtering cf. Budhiraja and Kallianpur 1995, In this work we study some properties of multiple Stratonovich integrals which also give some justification for the appearance of the name Stratonovich in the integral. We recall that one of the important properties of multiple Wiener-Itô integrals of symmetric kernels is that they can be expressed as iterated indefinite Itô-integrals, by which we mean that if f p L s[, 1] p the class of real valued, square integrable, symmetric functions defined on [, 1] p then the multiple Wiener integral of f p, I p f p can be expressed as: I p f p =p! 1 f p t 1,,t p dw tp dw t dw t It is also well known that a multiple Wiener integral of a symmetric kernel can be expressed in terms of iterated Skorohod integrals see Nualart and Zakai 1986

2 98 A. BUDHIRAJA AND G. KALLIANPUR for a detailed treatment of the Skorohod integral, i.e. if f p is as above, then: I p f p = f p t 1,,t p δw tp δw t δw t1, 1. where, δw t denotes the Skorohod integral. The first purpose of this work is to establish similar relationships of multiple Stratonovich integrals with single stochastic integrals. It will be shown that if the kernel possesses all the limiting traces and the first order traces are consistent with the second order traces see Section for definitions then the multiple Stratonovich integral, which is known to exist, can be expressed as an iterated integral as in 1. with the Skorohod integral replaced by the Ogawa integral. This result, in fact, holds more generally for the case of random kernels. We refer the reader to Budhiraja and Kallianpur 1997 for details. Our second result shows that if the kernel is continuous and all its limiting trace exist, so that the MSI exists, the MSI then can be expressed in terms of iterated indefinite Fisk-Stratonovich integrals. We remark that δ p need not exist for contiuous kernels. It is well known that cf. Johnson and Kallianpar 1993 a necessary and sufficient condition for the existence of δ p is that all the limiting traces exist. Examples can be given of continuous kernels for which the limiting trace do not exist. In view of this it becomes important to incorporate in Theorem 3.3 the condition for the existence of δ p. Furthermore, to ensure the existence of the Fisk-Stratonovich integral in Theorem 3.3 some smoothness assumption on the integrand is required, which in our set up translates into a condition on the continuity of the kernel. In the case of continuous kernels, it can be shown, in fact, that the MSI is the same as the iterated Stratonovich integral. The second purpose of this work is to study multiple Riemann-Stieltjes R- S integral approximations to multiple stochastic integrals. From the works of Wong and Zakai 1965 and Ikeda and Watanabe 1981 it is known that for certain smooth approximations of the Wiener process, the R-S integral of adapted square integrable processes with some additional restrictions with respect to the approximations, converges to the Fisk-Stratonovich integral as the approximating process converges to the Wiener process. In this work we show that the MSI possesses similar properties. For the Wong-Zakai approximation it is straightforward, using Theorem 5.1 of Johnson and Kallianpur 1993, that the corresponding R-S multiple integral converges in L Ω to the MSI. We state this fact in Section 4 without proof. We consider another approximation to the Wiener process called the mollifier approximation in Ikeda and Watanabe 1981 and show that the R-S integral with respect to this approximation converges to the MSI.

3 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS 99 The paper is organized as follows. In Section 1 we give a brief overview of single and multiple stochastic integrals. Section is devoted to obtaining representations for the MSI in terms of iterated stochastic integrals. Finally, in Section 3 we consider multiple R-S integrals with respect to some approximation of the Wiener process and obtain their convergence to multiple Stratonovich integrals as the approximation converges to the Wiener process.. Stochastic Integration: Single and Multiple Let Ω, F, P be a probability space. Assume F to be P-complete. We will denote by W t, t<1 a Wiener process on the probability space. Let L [a, b] p be the class of real valued square integrable functions defined on [a, b] p and by L s[a, b] p its subclass consisting of symmetric functions. The norm and inner product in L [a, b] p will be denoted by and, respectively without any reference to p. We begin this section with the definition of the Fisk-Stratonovich integral which is taken from Rosinski Definition.1. Let {X t,y t ; a t b} be stochastic processes. Let := {a = t 1 < <t m+1 = b} be a partition of [a, b]. The Fisk-Stratonovich integral of Y with respect to X, denoted by b a YodX, is defined as: b a YodX := lim m Y t i +Yt i+1 X ti+1 X ti,.1 where the above limit is taken in probability, provided it exists. A definition of a smoothed Stratonovich integral has been considered by Nualart and Zakai We remark on the connection of this integral with our work, later on in this section. Example.. Let f :[, 1] R be symmetric and continuous over the unit square. Then ft, sdw s odw t = ft, sdw s odw t = ft, sdw s dw t + ft, tdt.. Proof. We will show that the second Stratonovich integral in. equals the expression on the extreme right side in.. The proof for the first Stratonovich integral is similar. Let Y t := ft, sdw s and be a partition as in Definition

4 91 A. BUDHIRAJA AND G. KALLIANPUR.1. with a = and b = 1. Then a straightforward calculation shows: m Y t i +Yt i+1 W ti+1 W ti = 1 m [I 1,ti ] ft i,. 1 ti,t i+1 ] ] + 1 m [I 1,ti+1 ] ft i+1,. 1 ti,t i+1 ] ] + 1 m [ft i+1,t 1 ti,t i+1 ]t]dt..3 In view of continuity of f we have that: 1 m 1,ti ] ft i, 1 ti,t i+1 ] + 1 m 1,ti+1 ] ft i+1, 1 ti,t i+1 ] converges in L [, 1] to f, and 1 m 1 [ft i+1,t 1 ti,t i+1 ]t]dt converges to 1 ft, tdt as. The result now follows on taking the limit as in.3. The second integral that will concern us in this work is the Ogawa integral which we define below. The definition is again taken from Rosinski Definition.3. Let {X t ; t 1} be a measurable real valued stochastic process, such that E X t dt <. Suppose that for every complete orthonormal system CONS{φ i } of L [, 1] the series, n φ i tx t dt φ i tdw t.4 converges in L Ω, as n and that the limit is independent of the choice of the CONS. The limit is defined to be the Ogawa integral of X t and is denoted by: X t dw t. We remark that the Stratonovich integral considered in Nualart and Zakai 1989 is the same as the Ogawa integral if the latter exists. In Theorem 3.1 we prove that the multiple Stratonovich integral to be defined below is the same as an iterated Ogawa integral which will in turn imply that it is also the same as an iterated smoothed Stratonovich integral of Nualart and Zakai We now turn our attention to multiple stochastic integrals. The following treatment of multiple Stratonovich integrals has been adapted from Johnson and Kallianpur The key idea for defining the integral comes from the theory of lifting. A detailed discussion on lifting and its applications to prediction, filtering and smoothing can be found in Kallianpur and Karandikar Let F : L [a, b] R be a Borel cylinder function. Then k 1, h 1,,h k L [a, b] and a measurable function g : R k R such that for h L [a, b], F h =g h, h 1,, h, h k..5

5 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS 911 Associate with F a random variable R[F ] called the lifting of F, defined as: R[F ]ω :=gi 1 h 1 ω,,i 1 h k ω..6 The lifting for an arbitrary measurable function, F : L [, 1] R is defined starting from Borel cylinder functions, as follows. Suppose that for every finite dimensional projection π on L [a, b], F π is measurable with respect to the σ-field: {π 1 B :B BπH, the Borel class of the range of π} and suppose that for every sequence {π n } of finite dimensional projections converging to the identity operater strongly, the sequence {R[F π n ]} is a Cauchy sequence. Define the lifting of F denoted as R[F ] as the limit of this Cauchy sequence. It can be shown that the limit of R[F π n ] is independent of the choice of the sequence {π n }. The multiple Stratonovich integral is defined as follows. Definition.4. Let f p L s [a, b]p. Associate with f p amapψ p f p : L [a, b] R as follows. ψ p f p h :=<f p,h p >; h L [a, b]..7 Define the multiple Stratonovich integral of f p to be R[ψ p f p ], provided it exists. Denote it by δ p f p. We now give the definition of the traces introduced by Johnson and Kallianpur 1993 that relate the multiple Stratonovich integral to multiple Wiener integrals through the Hu-Meyer formula Definition.5. Let f p L s[a, b] p.fixk;1 k [ p/]. Suppose that for every CONS {φ i } of L [a, b], the series, N N i 1,i k =1 i k+1,i p=1 <f p,φ i1 φ i1 φ ik φ ik φ ik+1 φ ip >φ ik+1 φ ip.8 converges in L [a, b] p k to a limit which is independent of the choice of the CONS {φ i }. Then we say that the kth-limiting trace for f p exists, which by definition is the limit of the series in.8 and is denoted as Tr k f p. Tr f p is defined to be f p. The following theorem is the central result of Johnson and Kallianpur Theorem.6. Let f p L s [, 1]p. Then δ p f p exists iff Tr k f p exists k =, 1,,[ p/]. Moreover, where C p,k := p! p k! k k!. δ p f p = [ p/] k= C p,k I p k Tr k f p,.9

6 91 A. BUDHIRAJA AND G. KALLIANPUR Remark.7. We remark here that other definitions of MSI have been considered see for example Solé-Utzet 199, Zakai 199. These definitions are tied to a choice of a CONS in L [, 1]. In our work we are motivated to work with a MSI and corresponding traces which is invariant under the choice of a CONS. 3. Multiple Stratonovich Integral as an Iterated Stochastic Integral Though the original motivation for the name, multiple Stratonovich integral, for the integral introduced by Hu-Meyer came from the single Fisk-Stratonovich integral, its connection with the single Fisk-Stratonovich integral has not been brought out explicitely. In particular, it seems plausible that just as a multiple Itô-Wiener integral can be expressed as an iterated indefinite Itô integral,amul- tiple Stratonovich integral also should be expressible as some sort of an iterated stochastic integral. In this section we give some results regrading the representation of multiple Stratonovich integrals in terms of iterated Ogawa integrals and iterated Stratonovich integrals. Our first result shows that under the assumption of existence and consistency of first and second order limiting traces the multiple Stratonovich integral can be expressed as an iterated Ogawa integral. This result should be seen as the counterpart of the representation of multiple Wiener integrals in terms of iterated Skorohod integrals. For f p L s [, 1]p, we say that it has all the second order traces if Tr v Tr k f p exists v [p k/], k [ p/]. These second order traces are said to be consistent with the first order traces if Tr k Tr v f p = Tr k+v f p. Theorem 3.1. Let f p L s[, 1] p ; p>1 be such that all of its first and second order limiting traces exist and the second order traces are consistent with the first order traces. Then: f p t 1,,t p dw t1 dw tp 1 dw tp 3.1 exists and equals δ p f p. o Proof. We will show that if f p t 1,,t m,t m+1,t p dw t1 dw tm 1 dw t m = [m/] k= C m,k I m k Tr k f p., t m+1,t p 3. holds for m = j, then it holds for m = j +1, j<[ p/]. The result will then follow from Theorem.6 and the observation that 3. holds for m =1. Fix

7 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS j<[ p/] and suppose that 3. holds for m = j. Assume that j is odd. The case when j is even can be treated similarly and is omitted. Since by assumption Tr 1 Tr k f p exists k [j/], we have on using the consistency of traces see Theorem 4..3 of Budhiraja 1994 that: I j k Tr k f p., t j+1,,t p dw tj+1 exists and equals I j k+1 Tr k f p., t j+,,t p + j ki j k 1 Tr k+1 f p., t j+,,t p. Substituting the above equality in 3., we have from straightforward computations using the equality C j,k +j k +C j,k 1 = C j+1,k that: = [j+1/] k= This proves the theorem. f p t 1,,t j+1,t j+,,t p dw t1 dw tj dw tj+1 C j+1,k I j+1 k Tr k f p., t j+,,t p. Next we show that if the kernel is continuous and its multiple Stratonovich integral exists then this integral equals the iterated Stratonovich integral. To prove this result we need an auxiliary result given below. Lemma 3.. Let g p :[, 1] p R be a continuous symmetric function possessing all the limiting traces; then both I p 1g p., t dw t and I p 1g p., t odw t exist and are equal. The proof is omitted. Theorem 3.3. Let f p :[, 1] p R be a continuous symmetric function. Suppose that all the limiting traces of f p exist and the first order traces are consistent with the second order traces. Then the following equality holds: δ p f p = f p t 1,,t p dw t1 dw tp 1 dw tp = f p t 1,,t p odw t1 o dw tp 1 odw tp. Proof. Note initially that from Theorem 3.1 we have the validity of the first equality. For the second equality observe that f p t 1,,t p odw t1 = f p t 1,,t p dw t1.

8 914 A. BUDHIRAJA AND G. KALLIANPUR Now by iterating and observing that for j =1,,p: f p t 1,,t j,t j+1,,t p odwt 1 o dw tj 1 odw tj is a linear combination of multiple Wiener integrals with continuous kernels we have the result on applying Lemma 3.. Our main result in this section is the following theorem which gives a representation for multiple Stratonovich integrals in terms of iterated indefinite single Stratonovich integrals. This result is the counterpart of a similar representation result for multiple Itô-Wiener integrals in terms of iterated Itô integrals. Theorem 3.4. Let f p :[, 1] p R be a continuous symmetric function. Suppose that all the limiting traces of f p exist and the first order traces are consistent with the second order traces. Then the following equality holds: p δ p f p =p! f p t 1,,t p odw t1 o dw tp 1 odw tp. 3.3 The proof of the theorem requires the following lemma. Lemma 3.6. Let f p :[, 1] p R be a continuous symmetric function. Then, m p,t,t 1,t p m [, 1], the integrals: I mf p., t 1,,t p m, u, v odw u odw v and! v I mf p., t 1,,t p m,u,v odw u odw v exist and are equal, where the multiple Wiener integral I m is computed over [, 1] m. Proof. For the sake of notational simplicity we will denoted p m byp : m. Along the lines of example., it can be shown that I m f p., t 1,,t p:m,u,v odw u odw u exists and equals I m+1 f p., t 1,,t p:m,,v 1,t] odw v +m I m 1 f p., s, s, t t,,t p:m,vds odw v = I m+ f p., t 1,,t p:m,, 1 [,t], +mi m f p., s, s, t 1,,t p:m, ds 1,t] +I m f p., s, s, t 1,,t p:m ds +mm 1I m f p., s, s, u, u, t 1,,t p:m ds du. 3.4 [,t]

9 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS 915 Again, = v + m v I m 1 I m f p., t 1,,t p:m,u,v odw u odw v I m+1 f p., t 1,t p:m,,v 1,v] odw v f p., s, s, t 1,,t p:m,vds odw v. 3.5 Next let Π := { τ 1 τ τ m+1 = t} be a partition of [,t]. Consider: m I m+1 f p.,t 1,,t p:m,,τ i 1,τi ] +I m+1 f p.,t 1,,t p:m,,τ i+1 1,τi+1 ] W τi+1 W τi = 1 m I m+ f p., t 1,,t p:m,,τ i 1,τi ] 1 τi,τ i+1 ] + 1 m τ i+1 mi m f p., s, t 1,,t p:m,,τ i 1,τi ] ds τ i + 1 m I m+ f p., t 1,,t p:m,,τ i+1 1,τi+1 ] 1 τi,τ i+1 ] + 1 m τ i+1 mi m f p., s, t 1,,t p:m,,τ i+1 1,τi+1 ] ds τ i + 1 m τ i+1 I m f p., t 1,,t p:m,s,τ i+1 ds. 3.6 τ i Utilizing the continuity of f p and taking limit as Π, we have: I m+1 f p., t 1,t p:m,,v 1,v] odw v = 1 I t m+f p., t 1,,t p:m,, + mi m f p., s, s, t 1,,t p:m, ds + 1 I t m f p., t 1,,t p:m,s,sds. 3.7 In a similar fashion it is shown that: v m I m 1 f p., s, s, t 1,,t p:m,vds odw v = mi m f p., s, s, t 1,,t p:m, ds + 1 mm 1I m f p., s, s, u, u, t 1,,t p:m ds du. 3.8 [,t]

10 916 A. BUDHIRAJA AND G. KALLIANPUR Using 3.7 and 3.8 in 3.5, we have the result. Proof of Theorem 3.4. To prove the theorem, we will show that t [, 1] and j =,,p,the two integrals: j! j f pt 1,,t j,t j+1,,t p odw t1 o dw tj 1 o dw tj and f pt 1,,t j,t j+1,,t p o dw t1 odw tj 1 odw tj exist and are equal. The proof will be by an inductive argument on j. Note that by example., the assertion is clearly true when j =1,. Now assume that the assertion holds for j =1,,,p 1. Therefore f p t 1,,t p odw t1 odw tp 3 odw tp odw tp 1 p 1 =p 1! f p t 1,,t p odw t1 odw tp odw tp 1, which implies: =p 1! f p t 1,,t p odw t1 o dw tp 3 odw tp odw tp 1 odw tp f p t 1,,t p odw t1 odw tp odw tp 1 odw tp. 3.9 Note that the above integral does exist, in view of Theorem 3.3,.6 and Lemma 3.5. Observing that in view of the induction hypothesis and Theorems 3.3 and.6: p 1 f pt 1,,t p odw t1 dw tp is a linear combination of multiple Wiener integrals with continuous kernels, we have from Equation 3.9, on another application of Lemma 3.5 that: =p 1! p +p 1! =p 1! +p 1! p =p 1! +p 1 f p t 1,,t p odw t1 odw tp 3 odw tp odw tp 1 odw tp p! p f p t 1,,t p odw t1 odw tp odw tp 1 odw tp f p t 1,,t p odw t1 odw tp odw tp 1 f p t 1,,t p odw t1 odw tp odw tp 1 odw tp f p t 1,,t p odw t1 odw tp odw tp odw tp 1 f p t 1,,t p odw t1 odw tp 1 odw tp f p t 1,,t p odw t1 odw tp odw tp odw tp 1

11 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS 917 =p 1! +p 1 = p! p p p 1! p p f p t 1,,t p odw t1 o dw tp 1 odw tp f p t 1,,t p odw t1 odw tp odw tp odw tp 1 f p t 1,,t p odw t1 o dw tp 1 odw tp. 4. Multiple Stratonovich Integral as a Limit of Multiple Riemann- Stieltjes Integrals One question that naturally arises in defining stochastic integrals is the following. Suppose that the Wiener process is approximated by a certain smooth process so that it is meaningful to consider Riemann-Stieltjes integrals with respect to the approximation. Then what can be said about the limit of these Riemann-Stieltjes integrals as the approximation converges to the Wiener process? Questions of this nature were addressed by Wong and Zakai 1965 and Ikeda and Watanabe 1981 for single stochastic integrals and solutions of certain stochastic differential equations. It can be shown cf. Ikeda and Watanabe 1981 that for a certain class of adapted kernels and for some specialized class of approximations to the Wiener process the Riemann-Stieltjes integral converges to the Fisk-Stratonovich integral. The two most commonly studied approximations for which such a result is known are the mollifier approximations and the Wong-Zakai approximations see description below. In this section we consider a similar problem for multiple stochastic integrals. Questions of this nature have also been studied by Hu and Meyer 1993 though the precise definition of the MSI and the type of the approximations studied, differ from our work. We first consider the Wong-Zakai approximation. It will be seen in this case that the appearance of multiple Stratonovich integrals in the limit is a simple consequence of the definition of multiple Stratonovich integrals. Next we consider the mollifier approximation. For this approximation we restrict our attention to continuous kernels. It will be again seen that multiple Stratonovich integrals appear in the limit. We begin the section with an introduction to the Wong-Zakai and the mollifier approximation to the Wiener process. Wong-Zakai approximation. Let {φ i } be a CONS in L [, 1]. Define a sequence of stochastic processes: W n t, ω; t 1 as follows: n W n t := φ i sds φ i sdw s. It can easily be seen that W n t convergestow t, in L Ω, as n.

12 918 A. BUDHIRAJA AND G. KALLIANPUR Mollifier approximation. Let ρ : R R be a nonnegative C function whose support is contained in [, 1]. Also let, ρsds = 1. Define for ɛ>: ρ ɛ s := 1 ɛ ρs ɛ. Define the stochastic process B ɛ t, ω; t 1 as follows: B ɛ t, ω := W s, ωρ ɛ s tds = W s + t, ωρ ɛ sds. It can be seen cf. Johnson and Kallianpur 1993 that as ɛ, { E max W t B ɛt }. t 1 The following theorem for the Wong-Zakai approximation is a direct consequence of Theorem 5.1 of Johnson and Kallianpur The proof is omitted. Theorem 4.1. Let f p L s [, 1]p be such that all its limiting traces exist; then as n, the multiple Riemann-Stieltjes integral: f p t 1,,t p dw n t 1 dw n t p converges to δ p f p in L Ω. The following theorem is the main result of the section. Theorem 4.. Let f p :[, 1] p R be a continuous, symmetric function. Suppose that f p has all its limiting traces existing; then as ɛ the following Riemann- Stieltjes integral: f p t 1,,t p db ɛ t 1 db ɛ t p converges to δ p f p in L Ω. The proof of the theorem requires a few lemmas which we give below. We remark at this stage that until now we have defined δ p only for functions that are symmetric and the integral is computed over the set [, 1] p. Multiple Stratonovich integrals over the set [a, b] p can be defined in a similar manner and for the sake of notational simplicity we denote for a symmetric function f p : [a, b] p R, the MSI over [a, b] p again by δ p f p. Finally, we define the integral δ p f p for an element f p L [a, b] p to be the integral δ p f p, provided it exists, where f p is the symmetrization of f p. In the rest of the section we need to utilize the connection between δ p and the MSI studied by Solé and Utzet 199

13 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS 919 denoted in this work by δ s p. It is well known that if the kernel is continuous and has all the limiting traces satisfying the usual consistency conditions then the two MSI s exist and agree. We begin with the following lemma, for the proof of which we refer the reader to Budhiraja Lemma 4.3. Let f p :[a, b] p R be a function which is continuous everywhere excepting finitely many points. Then δ s pf p exists and if Π is a partition of [a, b] given as: Π:={a = t 1 <t <t m <t m+1 = b}, we have that m m δpf s p =L Ω lim f p t i1,,t ip i1 W ip W, Π i 1 =1 i p=1 where i W :=W t i+1 W t i. Moreover, E[δ s pf p ] = [p/] k= Cp,kp k! [a,b] p k [a,b] k g p t 1,t 1,,t k,t k,t k+1,,t p dt 1 dt k dtk+1 dt p, where g p := f p. Lemma 4.4. Let f p :[, 1] p R be a function which is continuous everywhere except finitely many points. Then for ɛ 1: f p u 1,,u p db ɛ u 1 db ɛ u p = o ρ ɛ v 1 ρ ɛ v p δpf s v 1,,v p p 1 v1,1+v 1 ] 1 vp,1+vp] dv 1 dv p, 4.1 where, f v 1,v p p u 1,,u p =f p u 1 v 1,,u p v p and the MSI, δp s is computed over [, ] p. Proof. We note initially that with g p = f v 1,v p p 1 v1,1+v 1 ] 1 vp,1+vp] and partitions Π as in Lemma 4.3, m m g p t i1,,t ip i1 W ip W i 1 =1 i p=1 converges in L Ω, uniformly in v 1,,v p which yields the joint measuribility in v 1,,v p,ω of δp sf v 1,,v p p 1 v1,1+v 1 ] 1 vp,1+vp]. Therefore the second integral in 4.1 is meaningful.

14 9 A. BUDHIRAJA AND G. KALLIANPUR Next, let Π n := { < 1/n < < n 1/n < } be a partition of [, ]. Observe that = L lim n f p u 1,,u p db ɛ u 1 dbɛu p ρ ɛ v 1 ρ ɛ v p n 1 i 1 =1 n 1 i p=1 f p i 1 n,,i p n W v 1+ i 1+1 n W v 1 + i 1 n Wv p+ i p+1 W v p + i p n n dv 1 dv p n 1 = L n 1 lim ρ ɛ v 1 ρ ɛ v p f p i 1 n n v 1,, i p n v p = i 1 =1 i p=1 1 v1,v 1 +1] i 1 n 1 i p vp,vp+1] n W i 1 +1 n W i 1 n W i p +1 n W i p n dv 1 dv p ρ ɛ v 1 ρ ɛ v p δp s f v 1,v p p 1 v1,1+v 1 ] 1 vp,1+v p] dv 1 dv p. Proof of Theorem 4.. Define for n 1, f n :[, 1] p R as follows. = f n,p t 1,,t p n 1 i 1 =1 Consider = n 1 i 1 =1 n 1 i p=1 n 1 i p=1 Note that for m 1, as ɛ, f p i 1 n,,i p n 1 i1 /n,i 1 +1/n]t 1 1 ip/n,i p+1/n]t p. f n,p t 1,t p db ɛ t 1 db ɛ t p f p i 1 n,,i p n B ɛ i 1+1 n B ɛ i 1 E B ɛ t W t m = E ɛ m 1 E Therefore, as ɛ, n B ɛ i p+1 n ρ ɛ s[w t + s W t]ds m ρ m ɛ s[w t + s W t] m ds C ɛ B ɛ i p. 4. n ɛm s m = C m +1. f n,p t 1,,t p db ɛ t 1 db ɛ t p L Ω δ s p f n,p =δ p f n,p. 4.3

15 TWO RESULTS ON MULTIPLE STRATONOVICH INTEGRALS 91 Also observe that from Lemma 4.3 and the condition on limiting traces, as n, δ p f n,p δ p f p. 4.4 Finally, we now show that for ɛ<1, as n, f n,p t 1,,t p db ɛ t 1 db ɛ t p L Ω f p t 1,,t p db ɛ t 1 db ɛ t p, uniformly in ɛ. Let δ> be arbitrary and let N 1 be such that for n N: f n,p t 1,,t p f p t 1,,t p <δ, t 1,,t p [, 1] p. 4.5 Define g n,p := f p f n,p. Then from Lemma 4.4, g n,p t 1,,t p db ɛ t 1 db ɛ t p = ρ ɛ v 1 ρ ɛ v p δp s g v 1,v p n,p 1 v1,1+v 1] 1 vp,1+vp] dv 1 dv p, where, g v 1,v p n,p u 1,,u p =g n,p u 1 v 1,,u p v p and the MSI, δp s is computed over [, ] p. Therefore, [ E C ɛ p ] g n,p t 1,,t p db ɛ t 1 db ɛ t p [ E δp s g v 1,v p n,p 1 v1,1+v 1] 1 dv1 vp,1+vp] ] dv p. 4.6 Using Lemma 4.3 and Eqution 4.5, we have, E[δ s pg v 1,v p n,p 1 v1,1+v 1 ] 1 vp,1+v p] ] [p/] δ k= C p,kp k! p k. 4.7 The theorem now follows on substituting 4.7 in 4.6. Acknowledgements This research was supported by the National Science Foundation and the Air Force Office of Scientific Research Grant No. F496 9 J 154 and the Army Research Office Grant No. DAAL3-9-G8.

16 9 A. BUDHIRAJA AND G. KALLIANPUR References Budhiraja, A. and Kallianpur, G Hilbert space valued traces and multiple Stratonovich integrals with statistical applications. Probab. Math. Statist., Jerzy Neyman Memorial issue. 15, Budhiraja, A Multiple stochastic integrals and Hilbert space valued traces with applications to asymptotic statistics and nonlinear filtering. Dissertation, The University of North Carolina Chapel Hill. Budhiraja, A. and Kallianpur, G A generalized Hu-Meyer formula for random kernels. Appl. Math. Optim. 35, Budhiraja, A. and Kallianpur, G Approximations to the solution of the Zakai equation using multiple Wiener and Stratonovich integral expansions. Stochastics Stochastic Rep. 56, Hu, Y. Z. and Meyer, P. A Sur les intégrales multiples de Stratonovich. Seminaire de Probabilités XXII, Université de Strasbourg, Lecture Notes in Math. 131, Hu, Y. Z. and Meyer, P. A Sur l approximation des multiples de Stratonovich. Stochastic Processes, in: A festschrift in honor of G. Kallianpur, eds: Cambanis, S., Karandikar, R. L., Sen, P. K., Ikeda, N. and Watanabe, S Stochastic Differential Equations and Diffusion Processes. Amsterdam-Oxford-New York. Johnson, G. W. and Kallianpur, G Homogeneous chaos, p-forms, scaling and the Feynman integral. Trans. Amer. Math. Soc. 34, Kallianpur, G. and Karandikar, R. L White Noise Theory of Prediction, Filtering and Smoothing. Stochastic monographs 3, Gordon and Breach, N.Y. Nualart, D. and Zakai, M Generalized stochastic integrals and Malliavin calculus. Probab. Theory Related Fields 73, Nualart, D. and Zakai, M The relation between the Stratonovich and Ogawa integrals. Ann. Probab. 17, Rosinski, J On stochastic integration by a series of Wiener integrals. Appl. Math. Optim. 19, Solé, J. L. and Utzet, F Stratonovich integral and trace. Stochastics Stochastic Rep. 9, 3-. Wong, E. and Zakai, M On the relation between ordinary and stochastic differential equations. Internat.J.Engrg.Sci.3, Zakai, M Stochastic integration, trace and the skeleton of Wiener functionals. Stochastics 3, Division of Applied Mathematics, Brown University, Providence, Rl 96, U.S.A. amarjit@cfm.brown.edu Department of Statistics, University of North Carolina, Chapel Hill, NC 7599, U.S.A. gk@stat.unc.edu Received June 1995; accepted November 1996

COVARIANCE IDENTITIES AND MIXING OF RANDOM TRANSFORMATIONS ON THE WIENER SPACE

COVARIANCE IDENTITIES AND MIXING OF RANDOM TRANSFORMATIONS ON THE WIENER SPACE Communications on Stochastic Analysis Vol. 4, No. 3 (21) 299-39 Serials Publications www.serialspublications.com COVARIANCE IDENTITIES AND MIXING OF RANDOM TRANSFORMATIONS ON THE WIENER SPACE NICOLAS PRIVAULT

More information

On pathwise stochastic integration

On pathwise stochastic integration On pathwise stochastic integration Rafa l Marcin Lochowski Afican Institute for Mathematical Sciences, Warsaw School of Economics UWC seminar Rafa l Marcin Lochowski (AIMS, WSE) On pathwise stochastic

More information

Man Kyu Im*, Un Cig Ji **, and Jae Hee Kim ***

Man Kyu Im*, Un Cig Ji **, and Jae Hee Kim *** JOURNAL OF THE CHUNGCHEONG MATHEMATICAL SOCIETY Volume 19, No. 4, December 26 GIRSANOV THEOREM FOR GAUSSIAN PROCESS WITH INDEPENDENT INCREMENTS Man Kyu Im*, Un Cig Ji **, and Jae Hee Kim *** Abstract.

More information

S. Lototsky and B.L. Rozovskii Center for Applied Mathematical Sciences University of Southern California, Los Angeles, CA

S. Lototsky and B.L. Rozovskii Center for Applied Mathematical Sciences University of Southern California, Los Angeles, CA RECURSIVE MULTIPLE WIENER INTEGRAL EXPANSION FOR NONLINEAR FILTERING OF DIFFUSION PROCESSES Published in: J. A. Goldstein, N. E. Gretsky, and J. J. Uhl (editors), Stochastic Processes and Functional Analysis,

More information

Regularity of the density for the stochastic heat equation

Regularity of the density for the stochastic heat equation Regularity of the density for the stochastic heat equation Carl Mueller 1 Department of Mathematics University of Rochester Rochester, NY 15627 USA email: cmlr@math.rochester.edu David Nualart 2 Department

More information

Itô s formula. Samy Tindel. Purdue University. Probability Theory 2 - MA 539

Itô s formula. Samy Tindel. Purdue University. Probability Theory 2 - MA 539 Itô s formula Samy Tindel Purdue University Probability Theory 2 - MA 539 Mostly taken from Brownian Motion and Stochastic Calculus by I. Karatzas and S. Shreve Samy T. Itô s formula Probability Theory

More information

Jae Gil Choi and Young Seo Park

Jae Gil Choi and Young Seo Park Kangweon-Kyungki Math. Jour. 11 (23), No. 1, pp. 17 3 TRANSLATION THEOREM ON FUNCTION SPACE Jae Gil Choi and Young Seo Park Abstract. In this paper, we use a generalized Brownian motion process to define

More information

The concentration of a drug in blood. Exponential decay. Different realizations. Exponential decay with noise. dc(t) dt.

The concentration of a drug in blood. Exponential decay. Different realizations. Exponential decay with noise. dc(t) dt. The concentration of a drug in blood Exponential decay C12 concentration 2 4 6 8 1 C12 concentration 2 4 6 8 1 dc(t) dt = µc(t) C(t) = C()e µt 2 4 6 8 1 12 time in minutes 2 4 6 8 1 12 time in minutes

More information

Independence of some multiple Poisson stochastic integrals with variable-sign kernels

Independence of some multiple Poisson stochastic integrals with variable-sign kernels Independence of some multiple Poisson stochastic integrals with variable-sign kernels Nicolas Privault Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological

More information

Fast-slow systems with chaotic noise

Fast-slow systems with chaotic noise Fast-slow systems with chaotic noise David Kelly Ian Melbourne Courant Institute New York University New York NY www.dtbkelly.com May 12, 215 Averaging and homogenization workshop, Luminy. Fast-slow systems

More information

Viscosity Iterative Approximating the Common Fixed Points of Non-expansive Semigroups in Banach Spaces

Viscosity Iterative Approximating the Common Fixed Points of Non-expansive Semigroups in Banach Spaces Viscosity Iterative Approximating the Common Fixed Points of Non-expansive Semigroups in Banach Spaces YUAN-HENG WANG Zhejiang Normal University Department of Mathematics Yingbing Road 688, 321004 Jinhua

More information

Homogeneous Stochastic Differential Equations

Homogeneous Stochastic Differential Equations WDS'1 Proceedings of Contributed Papers, Part I, 195 2, 21. ISBN 978-8-7378-139-2 MATFYZPRESS Homogeneous Stochastic Differential Equations J. Bártek Charles University, Faculty of Mathematics and Physics,

More information

Rough paths methods 4: Application to fbm

Rough paths methods 4: Application to fbm Rough paths methods 4: Application to fbm Samy Tindel Purdue University University of Aarhus 2016 Samy T. (Purdue) Rough Paths 4 Aarhus 2016 1 / 67 Outline 1 Main result 2 Construction of the Levy area:

More information

Citation Osaka Journal of Mathematics. 41(4)

Citation Osaka Journal of Mathematics. 41(4) TitleA non quasi-invariance of the Brown Authors Sadasue, Gaku Citation Osaka Journal of Mathematics. 414 Issue 4-1 Date Text Version publisher URL http://hdl.handle.net/1194/1174 DOI Rights Osaka University

More information

1 Brownian Local Time

1 Brownian Local Time 1 Brownian Local Time We first begin by defining the space and variables for Brownian local time. Let W t be a standard 1-D Wiener process. We know that for the set, {t : W t = } P (µ{t : W t = } = ) =

More information

Random Fields: Skorohod integral and Malliavin derivative

Random Fields: Skorohod integral and Malliavin derivative Dept. of Math. University of Oslo Pure Mathematics No. 36 ISSN 0806 2439 November 2004 Random Fields: Skorohod integral and Malliavin derivative Giulia Di Nunno 1 Oslo, 15th November 2004. Abstract We

More information

The Wiener Itô Chaos Expansion

The Wiener Itô Chaos Expansion 1 The Wiener Itô Chaos Expansion The celebrated Wiener Itô chaos expansion is fundamental in stochastic analysis. In particular, it plays a crucial role in the Malliavin calculus as it is presented in

More information

Discrete approximation of stochastic integrals with respect to fractional Brownian motion of Hurst index H > 1 2

Discrete approximation of stochastic integrals with respect to fractional Brownian motion of Hurst index H > 1 2 Discrete approximation of stochastic integrals with respect to fractional Brownian motion of urst index > 1 2 Francesca Biagini 1), Massimo Campanino 2), Serena Fuschini 2) 11th March 28 1) 2) Department

More information

Quasi-invariant measures on the path space of a diffusion

Quasi-invariant measures on the path space of a diffusion Quasi-invariant measures on the path space of a diffusion Denis Bell 1 Department of Mathematics, University of North Florida, 4567 St. Johns Bluff Road South, Jacksonville, FL 32224, U. S. A. email: dbell@unf.edu,

More information

Convergence of Particle Filtering Method for Nonlinear Estimation of Vortex Dynamics

Convergence of Particle Filtering Method for Nonlinear Estimation of Vortex Dynamics Convergence of Particle Filtering Method for Nonlinear Estimation of Vortex Dynamics Meng Xu Department of Mathematics University of Wyoming February 20, 2010 Outline 1 Nonlinear Filtering Stochastic Vortex

More information

An Infinitesimal Approach to Stochastic Analysis on Abstract Wiener Spaces

An Infinitesimal Approach to Stochastic Analysis on Abstract Wiener Spaces An Infinitesimal Approach to Stochastic Analysis on Abstract Wiener Spaces Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften an der Fakultät für Mathematik, Informatik

More information

Convergence at first and second order of some approximations of stochastic integrals

Convergence at first and second order of some approximations of stochastic integrals Convergence at first and second order of some approximations of stochastic integrals Bérard Bergery Blandine, Vallois Pierre IECN, Nancy-Université, CNRS, INRIA, Boulevard des Aiguillettes B.P. 239 F-5456

More information

Some Properties of NSFDEs

Some Properties of NSFDEs Chenggui Yuan (Swansea University) Some Properties of NSFDEs 1 / 41 Some Properties of NSFDEs Chenggui Yuan Swansea University Chenggui Yuan (Swansea University) Some Properties of NSFDEs 2 / 41 Outline

More information

Piecewise Smooth Solutions to the Burgers-Hilbert Equation

Piecewise Smooth Solutions to the Burgers-Hilbert Equation Piecewise Smooth Solutions to the Burgers-Hilbert Equation Alberto Bressan and Tianyou Zhang Department of Mathematics, Penn State University, University Park, Pa 68, USA e-mails: bressan@mathpsuedu, zhang

More information

A NOTE ON STOCHASTIC INTEGRALS AS L 2 -CURVES

A NOTE ON STOCHASTIC INTEGRALS AS L 2 -CURVES A NOTE ON STOCHASTIC INTEGRALS AS L 2 -CURVES STEFAN TAPPE Abstract. In a work of van Gaans (25a) stochastic integrals are regarded as L 2 -curves. In Filipović and Tappe (28) we have shown the connection

More information

Stochastic Integration and Stochastic Differential Equations: a gentle introduction

Stochastic Integration and Stochastic Differential Equations: a gentle introduction Stochastic Integration and Stochastic Differential Equations: a gentle introduction Oleg Makhnin New Mexico Tech Dept. of Mathematics October 26, 27 Intro: why Stochastic? Brownian Motion/ Wiener process

More information

Normal approximation of Poisson functionals in Kolmogorov distance

Normal approximation of Poisson functionals in Kolmogorov distance Normal approximation of Poisson functionals in Kolmogorov distance Matthias Schulte Abstract Peccati, Solè, Taqqu, and Utzet recently combined Stein s method and Malliavin calculus to obtain a bound for

More information

Large deviations and averaging for systems of slow fast stochastic reaction diffusion equations.

Large deviations and averaging for systems of slow fast stochastic reaction diffusion equations. Large deviations and averaging for systems of slow fast stochastic reaction diffusion equations. Wenqing Hu. 1 (Joint work with Michael Salins 2, Konstantinos Spiliopoulos 3.) 1. Department of Mathematics

More information

ON THE PATHWISE UNIQUENESS OF SOLUTIONS OF STOCHASTIC DIFFERENTIAL EQUATIONS

ON THE PATHWISE UNIQUENESS OF SOLUTIONS OF STOCHASTIC DIFFERENTIAL EQUATIONS PORTUGALIAE MATHEMATICA Vol. 55 Fasc. 4 1998 ON THE PATHWISE UNIQUENESS OF SOLUTIONS OF STOCHASTIC DIFFERENTIAL EQUATIONS C. Sonoc Abstract: A sufficient condition for uniqueness of solutions of ordinary

More information

Topics in fractional Brownian motion

Topics in fractional Brownian motion Topics in fractional Brownian motion Esko Valkeila Spring School, Jena 25.3. 2011 We plan to discuss the following items during these lectures: Fractional Brownian motion and its properties. Topics in

More information

DOOB S DECOMPOSITION THEOREM FOR NEAR-SUBMARTINGALES

DOOB S DECOMPOSITION THEOREM FOR NEAR-SUBMARTINGALES Communications on Stochastic Analysis Vol. 9, No. 4 (215) 467-476 Serials Publications www.serialspublications.com DOOB S DECOMPOSITION THEOREM FOR NEAR-SUBMARTINGALES HUI-HSIUNG KUO AND KIMIAKI SAITÔ*

More information

BOOK REVIEW. Review by Denis Bell. University of North Florida

BOOK REVIEW. Review by Denis Bell. University of North Florida BOOK REVIEW By Paul Malliavin, Stochastic Analysis. Springer, New York, 1997, 370 pages, $125.00. Review by Denis Bell University of North Florida This book is an exposition of some important topics in

More information

Admissible vector fields and quasi-invariant measures

Admissible vector fields and quasi-invariant measures Admissible vector fields and quasi-invariant measures Denis Bell Department of Mathematics, University of North Florida 4567 St. Johns Bluff Road South,Jacksonville, FL 32224, U. S. A. email: dbell@unf.edu

More information

Estimates for the density of functionals of SDE s with irregular drift

Estimates for the density of functionals of SDE s with irregular drift Estimates for the density of functionals of SDE s with irregular drift Arturo KOHATSU-HIGA a, Azmi MAKHLOUF a, a Ritsumeikan University and Japan Science and Technology Agency, Japan Abstract We obtain

More information

OPTIMAL SOLUTIONS TO STOCHASTIC DIFFERENTIAL INCLUSIONS

OPTIMAL SOLUTIONS TO STOCHASTIC DIFFERENTIAL INCLUSIONS APPLICATIONES MATHEMATICAE 29,4 (22), pp. 387 398 Mariusz Michta (Zielona Góra) OPTIMAL SOLUTIONS TO STOCHASTIC DIFFERENTIAL INCLUSIONS Abstract. A martingale problem approach is used first to analyze

More information

1. Stochastic Processes and filtrations

1. Stochastic Processes and filtrations 1. Stochastic Processes and 1. Stoch. pr., A stochastic process (X t ) t T is a collection of random variables on (Ω, F) with values in a measurable space (S, S), i.e., for all t, In our case X t : Ω S

More information

ON MATRIX VALUED SQUARE INTEGRABLE POSITIVE DEFINITE FUNCTIONS

ON MATRIX VALUED SQUARE INTEGRABLE POSITIVE DEFINITE FUNCTIONS 1 2 3 ON MATRIX VALUED SQUARE INTERABLE POSITIVE DEFINITE FUNCTIONS HONYU HE Abstract. In this paper, we study matrix valued positive definite functions on a unimodular group. We generalize two important

More information

THE SKOROKHOD OBLIQUE REFLECTION PROBLEM IN A CONVEX POLYHEDRON

THE SKOROKHOD OBLIQUE REFLECTION PROBLEM IN A CONVEX POLYHEDRON GEORGIAN MATHEMATICAL JOURNAL: Vol. 3, No. 2, 1996, 153-176 THE SKOROKHOD OBLIQUE REFLECTION PROBLEM IN A CONVEX POLYHEDRON M. SHASHIASHVILI Abstract. The Skorokhod oblique reflection problem is studied

More information

(2m)-TH MEAN BEHAVIOR OF SOLUTIONS OF STOCHASTIC DIFFERENTIAL EQUATIONS UNDER PARAMETRIC PERTURBATIONS

(2m)-TH MEAN BEHAVIOR OF SOLUTIONS OF STOCHASTIC DIFFERENTIAL EQUATIONS UNDER PARAMETRIC PERTURBATIONS (2m)-TH MEAN BEHAVIOR OF SOLUTIONS OF STOCHASTIC DIFFERENTIAL EQUATIONS UNDER PARAMETRIC PERTURBATIONS Svetlana Janković and Miljana Jovanović Faculty of Science, Department of Mathematics, University

More information

Homogenization for chaotic dynamical systems

Homogenization for chaotic dynamical systems Homogenization for chaotic dynamical systems David Kelly Ian Melbourne Department of Mathematics / Renci UNC Chapel Hill Mathematics Institute University of Warwick November 3, 2013 Duke/UNC Probability

More information

Maximum Likelihood Drift Estimation for Gaussian Process with Stationary Increments

Maximum Likelihood Drift Estimation for Gaussian Process with Stationary Increments Austrian Journal of Statistics April 27, Volume 46, 67 78. AJS http://www.ajs.or.at/ doi:.773/ajs.v46i3-4.672 Maximum Likelihood Drift Estimation for Gaussian Process with Stationary Increments Yuliya

More information

FE610 Stochastic Calculus for Financial Engineers. Stevens Institute of Technology

FE610 Stochastic Calculus for Financial Engineers. Stevens Institute of Technology FE610 Stochastic Calculus for Financial Engineers Lecture 3. Calculaus in Deterministic and Stochastic Environments Steve Yang Stevens Institute of Technology 01/31/2012 Outline 1 Modeling Random Behavior

More information

Wiener Chaos Solution of Stochastic Evolution Equations

Wiener Chaos Solution of Stochastic Evolution Equations Wiener Chaos Solution of Stochastic Evolution Equations Sergey V. Lototsky Department of Mathematics University of Southern California August 2003 Joint work with Boris Rozovskii The Wiener Chaos (Ω, F,

More information

Elementary properties of functions with one-sided limits

Elementary properties of functions with one-sided limits Elementary properties of functions with one-sided limits Jason Swanson July, 11 1 Definition and basic properties Let (X, d) be a metric space. A function f : R X is said to have one-sided limits if, for

More information

Hypoelliptic multiscale Langevin diffusions and Slow fast stochastic reaction diffusion equations.

Hypoelliptic multiscale Langevin diffusions and Slow fast stochastic reaction diffusion equations. Hypoelliptic multiscale Langevin diffusions and Slow fast stochastic reaction diffusion equations. Wenqing Hu. 1 (Joint works with Michael Salins 2 and Konstantinos Spiliopoulos 3.) 1. Department of Mathematics

More information

Derivatives of Harmonic Bergman and Bloch Functions on the Ball

Derivatives of Harmonic Bergman and Bloch Functions on the Ball Journal of Mathematical Analysis and Applications 26, 1 123 (21) doi:1.16/jmaa.2.7438, available online at http://www.idealibrary.com on Derivatives of Harmonic ergman and loch Functions on the all oo

More information

ESTIMATES OF DERIVATIVES OF THE HEAT KERNEL ON A COMPACT RIEMANNIAN MANIFOLD

ESTIMATES OF DERIVATIVES OF THE HEAT KERNEL ON A COMPACT RIEMANNIAN MANIFOLD PROCDINGS OF H AMRICAN MAHMAICAL SOCIY Volume 127, Number 12, Pages 3739 3744 S 2-9939(99)4967-9 Article electronically published on May 13, 1999 SIMAS OF DRIVAIVS OF H HA KRNL ON A COMPAC RIMANNIAN MANIFOLD

More information

Multi-Term Linear Fractional Nabla Difference Equations with Constant Coefficients

Multi-Term Linear Fractional Nabla Difference Equations with Constant Coefficients International Journal of Difference Equations ISSN 0973-6069, Volume 0, Number, pp. 9 06 205 http://campus.mst.edu/ijde Multi-Term Linear Fractional Nabla Difference Equations with Constant Coefficients

More information

Hypothesis testing for Stochastic PDEs. Igor Cialenco

Hypothesis testing for Stochastic PDEs. Igor Cialenco Hypothesis testing for Stochastic PDEs Igor Cialenco Department of Applied Mathematics Illinois Institute of Technology igor@math.iit.edu Joint work with Liaosha Xu Research partially funded by NSF grants

More information

The spectral zeta function

The spectral zeta function The spectral zeta function Bernd Ammann June 4, 215 Abstract In this talk we introduce spectral zeta functions. The spectral zeta function of the Laplace-Beltrami operator was already introduced by Minakshisundaram

More information

TRANSFORMATION FORMULA OF HIGHER ORDER INTEGRALS

TRANSFORMATION FORMULA OF HIGHER ORDER INTEGRALS J. Austral. Math. Soc. (Series A) 68 (2000), 155-164 TRANSFORMATION FORMULA OF HIGHER ORDER INTEGRALS TAO QIAN and TONGDE ZHONG (Received 21 December 1998; revised 30 June 1999) Communicated by P. G. Fenton

More information

Nonlinear Filtering Revisited: a Spectral Approach, II

Nonlinear Filtering Revisited: a Spectral Approach, II Nonlinear Filtering Revisited: a Spectral Approach, II Sergey V. Lototsky Center for Applied Mathematical Sciences University of Southern California Los Angeles, CA 90089-3 sergey@cams-00.usc.edu Remijigus

More information

Introduction to Infinite Dimensional Stochastic Analysis

Introduction to Infinite Dimensional Stochastic Analysis Introduction to Infinite Dimensional Stochastic Analysis By Zhi yuan Huang Department of Mathematics, Huazhong University of Science and Technology, Wuhan P. R. China and Jia an Yan Institute of Applied

More information

AN EXTENSION OF THE LAX-MILGRAM THEOREM AND ITS APPLICATION TO FRACTIONAL DIFFERENTIAL EQUATIONS

AN EXTENSION OF THE LAX-MILGRAM THEOREM AND ITS APPLICATION TO FRACTIONAL DIFFERENTIAL EQUATIONS Electronic Journal of Differential Equations, Vol. 215 (215), No. 95, pp. 1 9. ISSN: 172-6691. URL: http://ejde.math.txstate.edu or http://ejde.math.unt.edu ftp ejde.math.txstate.edu AN EXTENSION OF THE

More information

Brownian motion. Samy Tindel. Purdue University. Probability Theory 2 - MA 539

Brownian motion. Samy Tindel. Purdue University. Probability Theory 2 - MA 539 Brownian motion Samy Tindel Purdue University Probability Theory 2 - MA 539 Mostly taken from Brownian Motion and Stochastic Calculus by I. Karatzas and S. Shreve Samy T. Brownian motion Probability Theory

More information

Pathwise Construction of Stochastic Integrals

Pathwise Construction of Stochastic Integrals Pathwise Construction of Stochastic Integrals Marcel Nutz First version: August 14, 211. This version: June 12, 212. Abstract We propose a method to construct the stochastic integral simultaneously under

More information

EULER MARUYAMA APPROXIMATION FOR SDES WITH JUMPS AND NON-LIPSCHITZ COEFFICIENTS

EULER MARUYAMA APPROXIMATION FOR SDES WITH JUMPS AND NON-LIPSCHITZ COEFFICIENTS Qiao, H. Osaka J. Math. 51 (14), 47 66 EULER MARUYAMA APPROXIMATION FOR SDES WITH JUMPS AND NON-LIPSCHITZ COEFFICIENTS HUIJIE QIAO (Received May 6, 11, revised May 1, 1) Abstract In this paper we show

More information

1 Math 241A-B Homework Problem List for F2015 and W2016

1 Math 241A-B Homework Problem List for F2015 and W2016 1 Math 241A-B Homework Problem List for F2015 W2016 1.1 Homework 1. Due Wednesday, October 7, 2015 Notation 1.1 Let U be any set, g be a positive function on U, Y be a normed space. For any f : U Y let

More information

Universal examples. Chapter The Bernoulli process

Universal examples. Chapter The Bernoulli process Chapter 1 Universal examples 1.1 The Bernoulli process First description: Bernoulli random variables Y i for i = 1, 2, 3,... independent with P [Y i = 1] = p and P [Y i = ] = 1 p. Second description: Binomial

More information

VITA of Yaozhong HU. A. List of submitted papers

VITA of Yaozhong HU. A. List of submitted papers VITA of Yaozhong HU A. List of submitted papers 1. (with G. Rang) Parameter Estimation For Stochastic Hamiltonian Systems Driven By Fractional Brownian Motions. 2. (with G. Rang) Identification of the

More information

Malliavin Calculus: Analysis on Gaussian spaces

Malliavin Calculus: Analysis on Gaussian spaces Malliavin Calculus: Analysis on Gaussian spaces Josef Teichmann ETH Zürich Oxford 2011 Isonormal Gaussian process A Gaussian space is a (complete) probability space together with a Hilbert space of centered

More information

Fast-slow systems with chaotic noise

Fast-slow systems with chaotic noise Fast-slow systems with chaotic noise David Kelly Ian Melbourne Courant Institute New York University New York NY www.dtbkelly.com May 1, 216 Statistical properties of dynamical systems, ESI Vienna. David

More information

HAIYUN ZHOU, RAVI P. AGARWAL, YEOL JE CHO, AND YONG SOO KIM

HAIYUN ZHOU, RAVI P. AGARWAL, YEOL JE CHO, AND YONG SOO KIM Georgian Mathematical Journal Volume 9 (2002), Number 3, 591 600 NONEXPANSIVE MAPPINGS AND ITERATIVE METHODS IN UNIFORMLY CONVEX BANACH SPACES HAIYUN ZHOU, RAVI P. AGARWAL, YEOL JE CHO, AND YONG SOO KIM

More information

Information Structures Preserved Under Nonlinear Time-Varying Feedback

Information Structures Preserved Under Nonlinear Time-Varying Feedback Information Structures Preserved Under Nonlinear Time-Varying Feedback Michael Rotkowitz Electrical Engineering Royal Institute of Technology (KTH) SE-100 44 Stockholm, Sweden Email: michael.rotkowitz@ee.kth.se

More information

NORM DEPENDENCE OF THE COEFFICIENT MAP ON THE WINDOW SIZE 1

NORM DEPENDENCE OF THE COEFFICIENT MAP ON THE WINDOW SIZE 1 NORM DEPENDENCE OF THE COEFFICIENT MAP ON THE WINDOW SIZE Thomas I. Seidman and M. Seetharama Gowda Department of Mathematics and Statistics University of Maryland Baltimore County Baltimore, MD 8, U.S.A

More information

1 Lyapunov theory of stability

1 Lyapunov theory of stability M.Kawski, APM 581 Diff Equns Intro to Lyapunov theory. November 15, 29 1 1 Lyapunov theory of stability Introduction. Lyapunov s second (or direct) method provides tools for studying (asymptotic) stability

More information

A Concise Course on Stochastic Partial Differential Equations

A Concise Course on Stochastic Partial Differential Equations A Concise Course on Stochastic Partial Differential Equations Michael Röckner Reference: C. Prevot, M. Röckner: Springer LN in Math. 1905, Berlin (2007) And see the references therein for the original

More information

arxiv: v1 [math.pr] 1 May 2014

arxiv: v1 [math.pr] 1 May 2014 Submitted to the Brazilian Journal of Probability and Statistics A note on space-time Hölder regularity of mild solutions to stochastic Cauchy problems in L p -spaces arxiv:145.75v1 [math.pr] 1 May 214

More information

Quasi-invariant Measures on Path Space. Denis Bell University of North Florida

Quasi-invariant Measures on Path Space. Denis Bell University of North Florida Quasi-invariant Measures on Path Space Denis Bell University of North Florida Transformation of measure under the flow of a vector field Let E be a vector space (or a manifold), equipped with a finite

More information

be the set of complex valued 2π-periodic functions f on R such that

be the set of complex valued 2π-periodic functions f on R such that . Fourier series. Definition.. Given a real number P, we say a complex valued function f on R is P -periodic if f(x + P ) f(x) for all x R. We let be the set of complex valued -periodic functions f on

More information

2tdt 1 y = t2 + C y = which implies C = 1 and the solution is y = 1

2tdt 1 y = t2 + C y = which implies C = 1 and the solution is y = 1 Lectures - Week 11 General First Order ODEs & Numerical Methods for IVPs In general, nonlinear problems are much more difficult to solve than linear ones. Unfortunately many phenomena exhibit nonlinear

More information

White noise generalization of the Clark-Ocone formula under change of measure

White noise generalization of the Clark-Ocone formula under change of measure White noise generalization of the Clark-Ocone formula under change of measure Yeliz Yolcu Okur Supervisor: Prof. Bernt Øksendal Co-Advisor: Ass. Prof. Giulia Di Nunno Centre of Mathematics for Applications

More information

THE ANALYSIS OF SOLUTION OF NONLINEAR SECOND ORDER ORDINARY DIFFERENTIAL EQUATIONS

THE ANALYSIS OF SOLUTION OF NONLINEAR SECOND ORDER ORDINARY DIFFERENTIAL EQUATIONS THE ANALYSIS OF SOLUTION OF NONLINEAR SECOND ORDER ORDINARY DIFFERENTIAL EQUATIONS FANIRAN TAYE SAMUEL Assistant Lecturer, Department of Computer Science, Lead City University, Ibadan, Nigeria. Email :

More information

ON THE FIRST TIME THAT AN ITO PROCESS HITS A BARRIER

ON THE FIRST TIME THAT AN ITO PROCESS HITS A BARRIER ON THE FIRST TIME THAT AN ITO PROCESS HITS A BARRIER GERARDO HERNANDEZ-DEL-VALLE arxiv:1209.2411v1 [math.pr] 10 Sep 2012 Abstract. This work deals with first hitting time densities of Ito processes whose

More information

Properties of the Scattering Transform on the Real Line

Properties of the Scattering Transform on the Real Line Journal of Mathematical Analysis and Applications 58, 3 43 (001 doi:10.1006/jmaa.000.7375, available online at http://www.idealibrary.com on Properties of the Scattering Transform on the Real Line Michael

More information

Normal approximation of geometric Poisson functionals

Normal approximation of geometric Poisson functionals Institut für Stochastik Karlsruher Institut für Technologie Normal approximation of geometric Poisson functionals (Karlsruhe) joint work with Daniel Hug, Giovanni Peccati, Matthias Schulte presented at

More information

KOLMOGOROV DISTANCE FOR MULTIVARIATE NORMAL APPROXIMATION. Yoon Tae Kim and Hyun Suk Park

KOLMOGOROV DISTANCE FOR MULTIVARIATE NORMAL APPROXIMATION. Yoon Tae Kim and Hyun Suk Park Korean J. Math. 3 (015, No. 1, pp. 1 10 http://dx.doi.org/10.11568/kjm.015.3.1.1 KOLMOGOROV DISTANCE FOR MULTIVARIATE NORMAL APPROXIMATION Yoon Tae Kim and Hyun Suk Park Abstract. This paper concerns the

More information

On Reflecting Brownian Motion with Drift

On Reflecting Brownian Motion with Drift Proc. Symp. Stoch. Syst. Osaka, 25), ISCIE Kyoto, 26, 1-5) On Reflecting Brownian Motion with Drift Goran Peskir This version: 12 June 26 First version: 1 September 25 Research Report No. 3, 25, Probability

More information

New Identities for Weak KAM Theory

New Identities for Weak KAM Theory New Identities for Weak KAM Theory Lawrence C. Evans Department of Mathematics University of California, Berkeley Abstract This paper records for the Hamiltonian H = p + W (x) some old and new identities

More information

Reliability of Coherent Systems with Dependent Component Lifetimes

Reliability of Coherent Systems with Dependent Component Lifetimes Reliability of Coherent Systems with Dependent Component Lifetimes M. Burkschat Abstract In reliability theory, coherent systems represent a classical framework for describing the structure of technical

More information

Stability of Stochastic Differential Equations

Stability of Stochastic Differential Equations Lyapunov stability theory for ODEs s Stability of Stochastic Differential Equations Part 1: Introduction Department of Mathematics and Statistics University of Strathclyde Glasgow, G1 1XH December 2010

More information

FIXED POINT ITERATION FOR PSEUDOCONTRACTIVE MAPS

FIXED POINT ITERATION FOR PSEUDOCONTRACTIVE MAPS PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 127, Number 4, April 1999, Pages 1163 1170 S 0002-9939(99)05050-9 FIXED POINT ITERATION FOR PSEUDOCONTRACTIVE MAPS C. E. CHIDUME AND CHIKA MOORE

More information

Stochastic equations for thermodynamics

Stochastic equations for thermodynamics J. Chem. Soc., Faraday Trans. 93 (1997) 1751-1753 [arxiv 1503.09171] Stochastic equations for thermodynamics Roumen Tsekov Department of Physical Chemistry, University of Sofia, 1164 Sofia, ulgaria The

More information

BOUNDEDNESS OF SET-VALUED STOCHASTIC INTEGRALS

BOUNDEDNESS OF SET-VALUED STOCHASTIC INTEGRALS Discussiones Mathematicae Differential Inclusions, Control and Optimization 35 (2015) 197 207 doi:10.7151/dmdico.1173 BOUNDEDNESS OF SET-VALUED STOCHASTIC INTEGRALS Micha l Kisielewicz Faculty of Mathematics,

More information

for all f satisfying E[ f(x) ] <.

for all f satisfying E[ f(x) ] <. . Let (Ω, F, P ) be a probability space and D be a sub-σ-algebra of F. An (H, H)-valued random variable X is independent of D if and only if P ({X Γ} D) = P {X Γ}P (D) for all Γ H and D D. Prove that if

More information

Lévy Processes and Infinitely Divisible Measures in the Dual of afebruary Nuclear2017 Space 1 / 32

Lévy Processes and Infinitely Divisible Measures in the Dual of afebruary Nuclear2017 Space 1 / 32 Lévy Processes and Infinitely Divisible Measures in the Dual of a Nuclear Space David Applebaum School of Mathematics and Statistics, University of Sheffield, UK Talk at "Workshop on Infinite Dimensional

More information

Applications of Ito s Formula

Applications of Ito s Formula CHAPTER 4 Applications of Ito s Formula In this chapter, we discuss several basic theorems in stochastic analysis. Their proofs are good examples of applications of Itô s formula. 1. Lévy s martingale

More information

The Hilbert Transform and Fine Continuity

The Hilbert Transform and Fine Continuity Irish Math. Soc. Bulletin 58 (2006), 8 9 8 The Hilbert Transform and Fine Continuity J. B. TWOMEY Abstract. It is shown that the Hilbert transform of a function having bounded variation in a finite interval

More information

The Azéma-Yor Embedding in Non-Singular Diffusions

The Azéma-Yor Embedding in Non-Singular Diffusions Stochastic Process. Appl. Vol. 96, No. 2, 2001, 305-312 Research Report No. 406, 1999, Dept. Theoret. Statist. Aarhus The Azéma-Yor Embedding in Non-Singular Diffusions J. L. Pedersen and G. Peskir Let

More information

Pathwise volatility in a long-memory pricing model: estimation and asymptotic behavior

Pathwise volatility in a long-memory pricing model: estimation and asymptotic behavior Pathwise volatility in a long-memory pricing model: estimation and asymptotic behavior Ehsan Azmoodeh University of Vaasa Finland 7th General AMaMeF and Swissquote Conference September 7 1, 215 Outline

More information

arxiv: v1 [math.pr] 23 Jan 2018

arxiv: v1 [math.pr] 23 Jan 2018 TRANSFER PRINCIPLE FOR nt ORDER FRACTIONAL BROWNIAN MOTION WIT APPLICATIONS TO PREDICTION AND EQUIVALENCE IN LAW TOMMI SOTTINEN arxiv:181.7574v1 [math.pr 3 Jan 18 Department of Mathematics and Statistics,

More information

On non negative solutions of some quasilinear elliptic inequalities

On non negative solutions of some quasilinear elliptic inequalities On non negative solutions of some quasilinear elliptic inequalities Lorenzo D Ambrosio and Enzo Mitidieri September 28 2006 Abstract Let f : R R be a continuous function. We prove that under some additional

More information

Lecture No 1 Introduction to Diffusion equations The heat equat

Lecture No 1 Introduction to Diffusion equations The heat equat Lecture No 1 Introduction to Diffusion equations The heat equation Columbia University IAS summer program June, 2009 Outline of the lectures We will discuss some basic models of diffusion equations and

More information

#A69 INTEGERS 13 (2013) OPTIMAL PRIMITIVE SETS WITH RESTRICTED PRIMES

#A69 INTEGERS 13 (2013) OPTIMAL PRIMITIVE SETS WITH RESTRICTED PRIMES #A69 INTEGERS 3 (203) OPTIMAL PRIMITIVE SETS WITH RESTRICTED PRIMES William D. Banks Department of Mathematics, University of Missouri, Columbia, Missouri bankswd@missouri.edu Greg Martin Department of

More information

LAN property for sde s with additive fractional noise and continuous time observation

LAN property for sde s with additive fractional noise and continuous time observation LAN property for sde s with additive fractional noise and continuous time observation Eulalia Nualart (Universitat Pompeu Fabra, Barcelona) joint work with Samy Tindel (Purdue University) Vlad s 6th birthday,

More information

Stochastic Differential Equations.

Stochastic Differential Equations. Chapter 3 Stochastic Differential Equations. 3.1 Existence and Uniqueness. One of the ways of constructing a Diffusion process is to solve the stochastic differential equation dx(t) = σ(t, x(t)) dβ(t)

More information

On the martingales obtained by an extension due to Saisho, Tanemura and Yor of Pitman s theorem

On the martingales obtained by an extension due to Saisho, Tanemura and Yor of Pitman s theorem On the martingales obtained by an extension due to Saisho, Tanemura and Yor of Pitman s theorem Koichiro TAKAOKA Dept of Applied Physics, Tokyo Institute of Technology Abstract M Yor constructed a family

More information

GAUSSIAN PROCESSES; KOLMOGOROV-CHENTSOV THEOREM

GAUSSIAN PROCESSES; KOLMOGOROV-CHENTSOV THEOREM GAUSSIAN PROCESSES; KOLMOGOROV-CHENTSOV THEOREM STEVEN P. LALLEY 1. GAUSSIAN PROCESSES: DEFINITIONS AND EXAMPLES Definition 1.1. A standard (one-dimensional) Wiener process (also called Brownian motion)

More information

5 December 2016 MAA136 Researcher presentation. Anatoliy Malyarenko. Topics for Bachelor and Master Theses. Anatoliy Malyarenko

5 December 2016 MAA136 Researcher presentation. Anatoliy Malyarenko. Topics for Bachelor and Master Theses. Anatoliy Malyarenko 5 December 216 MAA136 Researcher presentation 1 schemes The main problem of financial engineering: calculate E [f(x t (x))], where {X t (x): t T } is the solution of the system of X t (x) = x + Ṽ (X s

More information

arxiv: v2 [math.pr] 22 Aug 2009

arxiv: v2 [math.pr] 22 Aug 2009 On the structure of Gaussian random variables arxiv:97.25v2 [math.pr] 22 Aug 29 Ciprian A. Tudor SAMOS/MATISSE, Centre d Economie de La Sorbonne, Université de Panthéon-Sorbonne Paris, 9, rue de Tolbiac,

More information