A. the Bochner Integral
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Arxiv:1910.08491V3 [Math.ST] 3 Nov 2020
Weakly stationary stochastic processes valued in a separable Hilbert space: Gramian-Cram´er representations and applications Amaury Durand ∗† Fran¸cois Roueff ∗ September 14, 2021 Abstract The spectral theory for weakly stationary processes valued in a separable Hilbert space has known renewed interest in the past decade. However, the recent literature on this topic is often based on restrictive assumptions or lacks important insights. In this paper, we follow earlier approaches which fully exploit the normal Hilbert module property of the space of Hilbert- valued random variables. This approach clarifies and completes the isomorphic relationship between the modular spectral domain to the modular time domain provided by the Gramian- Cram´er representation. We also discuss the general Bochner theorem and provide useful results on the composition and inversion of lag-invariant linear filters. Finally, we derive the Cram´er-Karhunen-Lo`eve decomposition and harmonic functional principal component analysis without relying on simplifying assumptions. 1 Introduction Functional data analysis has become an active field of research in the recent decades due to technological advances which makes it possible to store longitudinal data at very high frequency (see e.g. [22, 31]), or complex data e.g. in medical imaging [18, Chapter 9], [15], linguistics [28] or biophysics [27]. In these frameworks, the data is seen as valued in an infinite dimensional separable Hilbert space thus isomorphic to, and often taken to be, the function space L2(0, 1) of Lebesgue-square-integrable functions on [0, 1]. In this setting, a 2 functional time series refers to a bi-sequences (Xt)t∈Z of L (0, 1)-valued random variables and the assumption of finite second moment means that each random variable Xt belongs to the L2 Bochner space L2(Ω, F, L2(0, 1), P) of measurable mappings V : Ω → L2(0, 1) such that E 2 kV kL2(0,1) < ∞ , where k·kL2(0,1) here denotes the norm endowing the Hilbert space L2h(0, 1). -
Patterns in Random Walks and Brownian Motion
Patterns in Random Walks and Brownian Motion Jim Pitman and Wenpin Tang Abstract We ask if it is possible to find some particular continuous paths of unit length in linear Brownian motion. Beginning with a discrete version of the problem, we derive the asymptotics of the expected waiting time for several interesting patterns. These suggest corresponding results on the existence/non-existence of continuous paths embedded in Brownian motion. With further effort we are able to prove some of these existence and non-existence results by various stochastic analysis arguments. A list of open problems is presented. AMS 2010 Mathematics Subject Classification: 60C05, 60G17, 60J65. 1 Introduction and Main Results We are interested in the question of embedding some continuous-time stochastic processes .Zu;0Ä u Ä 1/ into a Brownian path .BtI t 0/, without time-change or scaling, just by a random translation of origin in spacetime. More precisely, we ask the following: Question 1 Given some distribution of a process Z with continuous paths, does there exist a random time T such that .BTCu BT I 0 Ä u Ä 1/ has the same distribution as .Zu;0Ä u Ä 1/? The question of whether external randomization is allowed to construct such a random time T, is of no importance here. In fact, we can simply ignore Brownian J. Pitman ()•W.Tang Department of Statistics, University of California, 367 Evans Hall, Berkeley, CA 94720-3860, USA e-mail: [email protected]; [email protected] © Springer International Publishing Switzerland 2015 49 C. Donati-Martin et al. -
Arxiv:Math/0409479V1
EXCEPTIONAL TIMES AND INVARIANCE FOR DYNAMICAL RANDOM WALKS DAVAR KHOSHNEVISAN, DAVID A. LEVIN, AND PEDRO J. MENDEZ-HERN´ ANDEZ´ n ABSTRACT. Consider a sequence Xi(0) of i.i.d. random variables. As- { }i=1 sociate to each Xi(0) an independent mean-one Poisson clock. Every time a clock rings replace that X-variable by an independent copy and restart the clock. In this way, we obtain i.i.d. stationary processes Xi(t) t 0 { } ≥ (i = 1, 2, ) whose invariant distribution is the law ν of X1(0). ··· Benjamini et al. (2003) introduced the dynamical walk Sn(t) = X1(t)+ + Xn(t), and proved among other things that the LIL holds for n ··· 7→ Sn(t) for all t. In other words, the LIL is dynamically stable. Subse- quently (2004b), we showed that in the case that the Xi(0)’s are standard normal, the classical integral test is not dynamically stable. Presently, we study the set of times t when n Sn(t) exceeds a given 7→ envelope infinitely often. Our analysis is made possible thanks to a con- nection to the Kolmogorov ε-entropy. When used in conjunction with the invariance principle of this paper, this connection has other interesting by-products some of which we relate. We prove also that the infinite-dimensional process t S n (t)/√n 7→ ⌊ •⌋ converges weakly in D(D([0, 1])) to the Ornstein–Uhlenbeck process in C ([0, 1]). For this we assume only that the increments have mean zero and variance one. In addition, we extend a result of Benjamini et al. -
Functions in the Fresnel Class of an Abstract Wiener Space
J. Korean Math. Soc. 28(1991), No. 2, pp. 245-265 FUNCTIONS IN THE FRESNEL CLASS OF AN ABSTRACT WIENER SPACE J.M. AHN*, G.W. JOHNSON AND D.L. SKOUG o. Introduction Abstract Wiener spaces have been of interest since the work of Gross in the early 1960's (see [18]) and are currently being used as a frame work for the Malliavin calculus (see the first half of [20]) and in the study of the Fresnel and Feynman integrals. It is this last topic which concerns us in this paper. The Feynman integral arose in nonrelativistic quantum mechanics and has been studied, with a variety of different definitions, by math ematicians and, in recent years, an increasing number of theoretical physicists. The Fresnel integral has been defined in Hilbert space [1], classical Wiener space [2], and abstract Wiener space [13] settings and used as an approach to the Feynman integral. Such approaches have their shortcomings; for example, the class offunctions that can be dealt with is somewhat limited. However, these approaches also have some distinct advantages including the fact, seen most clearly in [14], that several different definitions of the Feynman integral exist and agree. A key hypothesis in most of the theorems about the Fresnel and Feynman integrals is that the functions involved are in the 'Fresnel class' of the Hilbert space being used. Theorem 2 below is a general result insuring membership in :F(B), the Fresnel class of an abstract Wiener space B. :F(B) consists of all 'stochastic Fourier transforms' of certain Borel measures of finite variation. -
The Gaussian Radon Transform and Machine Learning
THE GAUSSIAN RADON TRANSFORM AND MACHINE LEARNING IRINA HOLMES AND AMBAR N. SENGUPTA Abstract. There has been growing recent interest in probabilistic interpretations of kernel- based methods as well as learning in Banach spaces. The absence of a useful Lebesgue measure on an infinite-dimensional reproducing kernel Hilbert space is a serious obstacle for such stochastic models. We propose an estimation model for the ridge regression problem within the framework of abstract Wiener spaces and show how the support vector machine solution to such problems can be interpreted in terms of the Gaussian Radon transform. 1. Introduction A central task in machine learning is the prediction of unknown values based on `learning' from a given set of outcomes. More precisely, suppose X is a non-empty set, the input space, and D = f(p1; y1); (p2; y2);:::; (pn; yn)g ⊂ X × R (1.1) is a finite collection of input values pj together with their corresponding real outputs yj. The goal is to predict the value y corresponding to a yet unobserved input value p 2 X . The fundamental assumption of kernel-based methods such as support vector machines (SVM) is that the predicted value is given by f^(p), where the decision or prediction function f^ belongs to a reproducing kernel Hilbert space (RKHS) H over X , corresponding to a positive definite function K : X × X ! R (see Section 2.1.1). In particular, in ridge regression, the predictor ^ function fλ is the solution to the minimization problem: n ! ^ X 2 2 fλ = arg min (yj − f(pj)) + λkfk ; (1.2) f2H j=1 where λ > 0 is a regularization parameter and kfk denotes the norm of f in H. -
Change of Variables in Infinite-Dimensional Space
Change of variables in infinite-dimensional space Denis Bell Denis Bell Change of variables in infinite-dimensional space Change of variables formula in R Let φ be a continuously differentiable function on [a; b] and f an integrable function. Then Z φ(b) Z b f (y)dy = f (φ(x))φ0(x)dx: φ(a) a Denis Bell Change of variables in infinite-dimensional space Higher dimensional version Let φ : B 7! φ(B) be a diffeomorphism between open subsets of Rn. Then the change of variables theorem reads Z Z f (y)dy = (f ◦ φ)(x)Jφ(x)dx: φ(B) B where @φj Jφ = det @xi is the Jacobian of the transformation φ. Denis Bell Change of variables in infinite-dimensional space Set f ≡ 1 in the change of variables formula. Then Z Z dy = Jφ(x)dx φ(B) B i.e. Z λ(φ(B)) = Jφ(x)dx: B where λ denotes the Lebesgue measure (volume). Denis Bell Change of variables in infinite-dimensional space Version for Gaussian measure in Rn − 1 jjxjj2 Take f (x) = e 2 in the change of variables formula and write φ(x) = x + K(x). Then we obtain Z Z − 1 jjyjj2 <K(x);x>− 1 jK(x)j2 − 1 jjxjj2 e 2 dy = e 2 Jφ(x)e 2 dx: φ(B) B − 1 jxj2 So, denoting by γ the Gaussian measure dγ = e 2 dx, we have Z <K(x);x>− 1 jjKx)jj2 γ(φ(B)) = e 2 Jφ(x)dγ: B Denis Bell Change of variables in infinite-dimensional space Infinite-dimesnsions There is no analogue of the Lebesgue measure in an infinite-dimensional vector space. -
Bochner Integrals and Vector Measures
Michigan Technological University Digital Commons @ Michigan Tech Dissertations, Master's Theses and Master's Reports 1993 Bochner Integrals and Vector Measures Ivaylo D. Dinov Michigan Technological University Copyright 1993 Ivaylo D. Dinov Recommended Citation Dinov, Ivaylo D., "Bochner Integrals and Vector Measures", Open Access Master's Report, Michigan Technological University, 1993. https://doi.org/10.37099/mtu.dc.etdr/919 Follow this and additional works at: https://digitalcommons.mtu.edu/etdr Part of the Mathematics Commons “BOCHNER INTEGRALS AND VECTOR MEASURES” Project for the Degree of M.S. MICHIGAN TECH UNIVERSITY IVAYLO D. DINOV BOCHNER INTEGRALS RND VECTOR MEASURES By IVAYLO D. DINOV A REPORT (PROJECT) Submitted in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN MATHEMATICS Spring 1993 MICHIGAN TECHNOLOGICRL UNIUERSITV HOUGHTON, MICHIGAN U.S.R. 4 9 9 3 1 -1 2 9 5 . Received J. ROBERT VAN PELT LIBRARY APR 2 0 1993 MICHIGAN TECHNOLOGICAL UNIVERSITY I HOUGHTON, MICHIGAN GRADUATE SCHOOL MICHIGAN TECH This Project, “Bochner Integrals and Vector Measures”, is hereby approved in partial fulfillment of the requirements for the degree of MASTER OF SCIENCE IN MATHEMATICS. DEPARTMENT OF MATHEMATICAL SCIENCES MICHIGAN TECHNOLOGICAL UNIVERSITY Project A d v is o r Kenneth L. Kuttler Head of Department— Dr.Alphonse Baartmans 2o April 1992 Date •vaylo D. Dinov “Bochner Integrals and Vector Measures” 1400 TOWNSEND DRIVE. HOUGHTON Ml 49931-1295 flskngiyledgtiients I wish to express my appreciation to my advisor, Dr. Kenneth L. Kuttler, for his help, guidance and direction in the preparation of this report. The corrections and the revisions that he suggested made the project look complete and easy to read. -
Graduate Research Statement
Research Statement Irina Holmes October 2013 My current research interests lie in infinite-dimensional analysis and geometry, probability and statistics, and machine learning. The main focus of my graduate studies has been the development of the Gaussian Radon transform for Banach spaces, an infinite-dimensional generalization of the classical Radon transform. This transform and some of its properties are discussed in Sections 2 and 3.1 below. Most recently, I have been studying applications of the Gaussian Radon transform to machine learning, an aspect discussed in Section 4. 1 Background The Radon transform was first developed by Johann Radon in 1917. For a function f : Rn ! R the Radon transform is the function Rf defined on the set of all hyperplanes P in n given by: R Z Rf(P ) def= f dx; P where, for every P , integration is with respect to Lebesgue measure on P . If we think of the hyperplane P as a \ray" shooting through the support of f, the in- tegral of f over P can be viewed as a way to measure the changes in the \density" of P f as the ray passes through it. In other words, Rf may be used to reconstruct the f(x, y) density of an n-dimensional object from its (n − 1)-dimensional cross-sections in differ- ent directions. Through this line of think- ing, the Radon transform became the math- ematical background for medical CT scans, tomography and other image reconstruction applications. Besides the intrinsic mathematical and Figure 1: The Radon Transform theoretical value, a practical motivation for our work is the ability to obtain information about a function defined on an infinite-dimensional space from its conditional expectations. -
Methods for Analysis of Functionals on Gaussian Self Similar Processes
METHODS FOR ANALYSIS OF FUNCTIONALS ON GAUSSIAN SELF SIMILAR PROCESSES A Thesis submitted by Hirdyesh Bhatia For the award of Doctor of Philosophy 2018 Abstract Many engineering and scientific applications necessitate the estimation of statistics of various functionals on stochastic processes. In Chapter 2, Norros et al’s Girsanov theorem for fBm is reviewed and extended to allow for non-unit volatility. We then prove that using method of images to solve the Fokker-Plank/Kolmogorov equation with a Dirac delta initial condition and a Dirichlet boundary condition to evaluate the first passage density, does not work in the case of fBm. Chapter3 provides generalisation of both the theorem of Ramer which finds a for- mula for the Radon-Nikodym derivative of a transformed Gaussian measure and of the Girsanov theorem. A P -measurable derivative of a P -measurable function is defined and then shown to coincide with the stochastic derivative, under certain assumptions, which in turn coincides with the Malliavin derivative when both are defined. In Chap- ter4 consistent quasi-invariant stochastic flows are defined. When such a flow trans- forms a certain functional consistently a simple formula exists for the density of that functional. This is then used to derive the last exit distribution of Brownian motion. In Chapter5 a link between the probability density function of an approximation of the supremum of fBm with drift and the Generalised Gamma distribution is established. Finally the self-similarity induced on the distributions of the sup and the first passage functionals on fBm with linear drift are shown to imply the existence of transport equations on the family of these densities as the drift varies. -
BOCHNER Vs. PETTIS NORM: EXAMPLES and RESULTS 3
Contemp orary Mathematics Volume 00, 0000 Bo chner vs. Pettis norm: examples and results S.J. DILWORTH AND MARIA GIRARDI Contemp. Math. 144 1993 69{80 Banach Spaces, Bor-Luh Lin and Wil liam B. Johnson, editors Abstract. Our basic example shows that for an arbitrary in nite-dimen- sional Banach space X, the Bo chner norm and the Pettis norm on L X are 1 not equivalent. Re nements of this example are then used to investigate various mo des of sequential convergence in L X. 1 1. INTRODUCTION Over the years, the Pettis integral along with the Pettis norm have grabb ed the interest of many. In this note, we wish to clarify the di erences b etween the Bo chner and the Pettis norms. We b egin our investigation by using Dvoretzky's Theorem to construct, for an arbitrary in nite-dimensional Banach space, a se- quence of Bo chner integrable functions whose Bo chner norms tend to in nity but whose Pettis norms tend to zero. By re ning this example again working with an arbitrary in nite-dimensional Banach space, we pro duce a Pettis integrable function that is not Bo chner integrable and we show that the space of Pettis integrable functions is not complete. Thus our basic example provides a uni- ed constructiveway of seeing several known facts. The third section expresses these results from a vector measure viewp oint. In the last section, with the aid of these examples, we give a fairly thorough survey of the implications going between various mo des of convergence for sequences of L X functions. -
A Limiting Process to Invert the Gauss-Radon Transform
Communications on Stochastic Analysis Vol. 13, No. 1-2, (2019) A LIMITING PROCESS TO INVERT THE GAUSS-RADON TRANSFORM JEREMY J. BECNEL* Abstract. In this work we extend the finite dimensional Radon transform [23] to the Gaussian measure. We develop an inversion formula for this Gauss- Radon transform by way of Fourier inversion formula. We then proceed to extend these results to the infinite dimensional setting. 1. Introduction The Radon transform was invented by Johann Radon in 1917 [23]. The Radon n transform of a suitable function f : R R is defined as a function f on the set → R Pn of hyperplanes in Rn as follows (αv + v⊥)= f(x) dx, (1.1) Rf ∫αv+v⊥ where dx is the Lebesgue measure on the hyperplane given by αv + v⊥. The Radon transform remains a useful and important tool even today because it has applications to many fields, included tomography and medicine [10]. Some of the primary results related to the Radon transform involve the Support Theorem and the various inversion formulas. Using the Laplacian operator or the Fourier transform one can actually recover a function f from the Radon transform f [14]. The is one of the primary reasons the Radon transform has proved so usefulR in many applications. This transform does not generalize directly to infinite dimensions because there is no useful notion of Lebesgue measure in infinite dimensions. However, there is a well-developed theory of Gaussian measures in infinite dimensions and so it is natural to extend the Radon transform to infinite dimensions using Gaussian measure: Gf (P )= f dµP , (1.2) ∫ where µP is the Gaussian measure on any infinite dimensional hyperplane P in a Hilbert space H0. -
A Riesz Representation Theorem for the Space of Henstock Integrable Vector-Valued Functions
Hindawi Journal of Function Spaces Volume 2018, Article ID 8169565, 9 pages https://doi.org/10.1155/2018/8169565 Research Article A Riesz Representation Theorem for the Space of Henstock Integrable Vector-Valued Functions Tomás Pérez Becerra ,1 Juan Alberto Escamilla Reyna,1 Daniela Rodríguez Tzompantzi,1 Jose Jacobo Oliveros Oliveros ,1 and Khaing Khaing Aye2 1 Facultad de Ciencias F´ısico Matematicas,´ Benemerita´ Universidad AutonomadePuebla,Puebla,PUE,Mexico´ 2Department of Engineering Mathematics, Yangon Technological University, Yangon, Myanmar Correspondence should be addressed to Tomas´ Perez´ Becerra; [email protected] Received 15 January 2018; Revised 21 March 2018; Accepted 5 April 2018; Published 17 May 2018 Academic Editor: Adrian Petrusel Copyright © 2018 Tomas´ Perez´ Becerra et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Using a bounded bilinear operator, we defne the Henstock-Stieltjes integral for vector-valued functions; we prove some integration by parts theorems for Henstock integral and a Riesz-type theorem which provides an alternative proof of the representation theorem for real functions proved by Alexiewicz. 1. Introduction 2. Preliminaries Henstock in [1] defnes a Riemann type integral which Troughout this paper �, �,and� will denote three Banach is equivalent to Denjoy integral and more general than spaces, ‖⋅‖�, ‖⋅‖�,and‖⋅‖�, which will denote their respective ∗ the Lebesgue integral, called the Henstock integral. Cao in norms, � the dual of �, �:�×�→�a bounded bilinear [2] extends the Henstock integral for vector-valued func- operator fxed, and [�, �] aclosedfniteintervaloftherealline tions and provides some basic properties such as the Saks- with the usual topology and the Lebesgue measure, which we Henstock Lemma.