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Complex Measures 1 11
Tutorial 11: Complex Measures 1 11. Complex Measures In the following, (Ω, F) denotes an arbitrary measurable space. Definition 90 Let (an)n≥1 be a sequence of complex numbers. We a say that ( n)n≥1 has the permutation property if and only if, for ∗ ∗ +∞ 1 all bijections σ : N → N ,theseries k=1 aσ(k) converges in C Exercise 1. Let (an)n≥1 be a sequence of complex numbers. 1. Show that if (an)n≥1 has the permutation property, then the same is true of (Re(an))n≥1 and (Im(an))n≥1. +∞ 2. Suppose an ∈ R for all n ≥ 1. Show that if k=1 ak converges: +∞ +∞ +∞ + − |ak| =+∞⇒ ak = ak =+∞ k=1 k=1 k=1 1which excludes ±∞ as limit. www.probability.net Tutorial 11: Complex Measures 2 Exercise 2. Let (an)n≥1 be a sequence in R, such that the series +∞ +∞ k=1 ak converges, and k=1 |ak| =+∞.LetA>0. We define: + − N = {k ≥ 1:ak ≥ 0} ,N = {k ≥ 1:ak < 0} 1. Show that N + and N − are infinite. 2. Let φ+ : N∗ → N + and φ− : N∗ → N − be two bijections. Show the existence of k1 ≥ 1 such that: k1 aφ+(k) ≥ A k=1 3. Show the existence of an increasing sequence (kp)p≥1 such that: kp aφ+(k) ≥ A k=kp−1+1 www.probability.net Tutorial 11: Complex Measures 3 for all p ≥ 1, where k0 =0. 4. Consider the permutation σ : N∗ → N∗ defined informally by: φ− ,φ+ ,...,φ+ k ,φ− ,φ+ k ,...,φ+ k ,.. -
Appendix A. Measure and Integration
Appendix A. Measure and integration We suppose the reader is familiar with the basic facts concerning set theory and integration as they are presented in the introductory course of analysis. In this appendix, we review them briefly, and add some more which we shall need in the text. Basic references for proofs and a detailed exposition are, e.g., [[ H a l 1 ]] , [[ J a r 1 , 2 ]] , [[ K F 1 , 2 ]] , [[ L i L ]] , [[ R u 1 ]] , or any other textbook on analysis you might prefer. A.1 Sets, mappings, relations A set is a collection of objects called elements. The symbol card X denotes the cardi- nality of the set X. The subset M consisting of the elements of X which satisfy the conditions P1(x),...,Pn(x) is usually written as M = { x ∈ X : P1(x),...,Pn(x) }.A set whose elements are certain sets is called a system or family of these sets; the family of all subsystems of a given X is denoted as 2X . The operations of union, intersection, and set difference are introduced in the standard way; the first two of these are commutative, associative, and mutually distributive. In a { } system Mα of any cardinality, the de Morgan relations , X \ Mα = (X \ Mα)and X \ Mα = (X \ Mα), α α α α are valid. Another elementary property is the following: for any family {Mn} ,whichis { } at most countable, there is a disjoint family Nn of the same cardinality such that ⊂ \ ∪ \ Nn Mn and n Nn = n Mn.Theset(M N) (N M) is called the symmetric difference of the sets M,N and denoted as M #N. -
Product Integration
Product Integration Richard D. Gill Mathematical Institute, University of Utrecht, Netherlands EURANDOM, Eindhoven, Netherlands May 24, 2001 Abstract This is a brief survey of product-integration for statisticians. All statisticians are familiar with the sum and product symbols and , and with the integral symbol . Also they are aware that there is a certain anal- ogy between summation and integration; in fact the integral symbolPis nothingQ else than a stretched-out capitalR S|the S of summation. Strange therefore that not many people are aware of the existence of the product-integral P, invented by the Italian mathematician Vito Volterra in 1887, which bears exactly the same relation to the ordinary product as the integral does to summation. The mathematical theory of product-integration is not terribly difficult and not terribly deep, which is perhaps one of the reasons it was out of fashion again by the time survival analysis came into being in the fifties. However it is terribly useful and it is a pity that Kaplan and Meier (1958), the inventors of the product-limit or Kaplan-Meier estimator (the nonparametric maximum likelihood estimator of an unknown distribution function based on a sample of censored survival times), did not make the connection, as neither did the au- thors of the classic and papers on this estimator, Efron (1967) and Breslow and Crowley (1974). Only with the Aalen and Johansen (1978) was the connection between the Kaplan-Meier estimator and product-integration made explicit. It took several more years before the connection was put to use to derive new large sample properties of the Kaplan-Meier estimator (e.g., the asymptotic normality of the Kaplan-Meier mean) in Gill (1983). -
About and Beyond the Lebesgue Decomposition of a Signed Measure
Lecturas Matemáticas Volumen 37 (2) (2016), páginas 79-103 ISSN 0120-1980 About and beyond the Lebesgue decomposition of a signed measure Sobre el teorema de descomposición de Lebesgue de una medida con signo y un poco más Josefina Alvarez1 and Martha Guzmán-Partida2 1New Mexico State University, EE.UU. 2Universidad de Sonora, México ABSTRACT. We present a refinement of the Lebesgue decomposition of a signed measure. We also revisit the notion of mutual singularity for finite signed measures, explaining its connection with a notion of orthogonality introduced by R.C. JAMES. Key words: Lebesgue Decomposition Theorem, Mutually Singular Signed Measures, Orthogonality. RESUMEN. Presentamos un refinamiento de la descomposición de Lebesgue de una medida con signo. También revisitamos la noción de singularidad mutua para medidas con signo finitas, explicando su relación con una noción de ortogonalidad introducida por R. C. JAMES. Palabras clave: Teorema de Descomposición de Lebesgue, Medidas con Signo Mutua- mente Singulares, Ortogonalidad. 2010 AMS Mathematics Subject Classification. Primary 28A10; Secondary 28A33, Third 46C50. 1. Introduction The main goal of this expository article is to present a refinement, not often found in the literature, of the Lebesgue decomposition of a signed measure. For the sake of completeness, we begin with a collection of preliminary definitions and results, including many comments, examples and counterexamples. We continue with a section dedicated 80 Josefina Alvarez et al. About and beyond the Lebesgue decomposition... specifically to the Lebesgue decomposition of a signed measure, where we also present a short account of its historical development. Next comes the centerpiece of our exposition, a refinement of the Lebesgue decomposition of a signed measure, which we prove in detail. -
Stochastic Integral Equations Without Probability Author(S): Thomas Mikosch and Rimas Norvaiša Source: Bernoulli, Vol
International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics and Probability Stochastic Integral Equations without Probability Author(s): Thomas Mikosch and Rimas Norvaiša Source: Bernoulli, Vol. 6, No. 3 (Jun., 2000), pp. 401-434 Published by: International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics and Probability Stable URL: http://www.jstor.org/stable/3318668 Accessed: 20/04/2010 10:28 Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/action/showPublisher?publisherCode=isibs. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact [email protected]. International Statistical Institute (ISI) and Bernoulli Society for Mathematical Statistics and Probability is collaborating with JSTOR to digitize, preserve and extend access to Bernoulli. -
(Measure Theory for Dummies) UWEE Technical Report Number UWEETR-2006-0008
A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 UWEE Technical Report Number UWEETR-2006-0008 May 2006 Department of Electrical Engineering University of Washington Box 352500 Seattle, Washington 98195-2500 PHN: (206) 543-2150 FAX: (206) 543-3842 URL: http://www.ee.washington.edu A Measure Theory Tutorial (Measure Theory for Dummies) Maya R. Gupta {gupta}@ee.washington.edu Dept of EE, University of Washington Seattle WA, 98195-2500 University of Washington, Dept. of EE, UWEETR-2006-0008 May 2006 Abstract This tutorial is an informal introduction to measure theory for people who are interested in reading papers that use measure theory. The tutorial assumes one has had at least a year of college-level calculus, some graduate level exposure to random processes, and familiarity with terms like “closed” and “open.” The focus is on the terms and ideas relevant to applied probability and information theory. There are no proofs and no exercises. Measure theory is a bit like grammar, many people communicate clearly without worrying about all the details, but the details do exist and for good reasons. There are a number of great texts that do measure theory justice. This is not one of them. Rather this is a hack way to get the basic ideas down so you can read through research papers and follow what’s going on. Hopefully, you’ll get curious and excited enough about the details to check out some of the references for a deeper understanding. -
On the Curvature of Piecewise Flat Spaces
Communications in Commun. Math. Phys. 92, 405-454 (1984) Mathematical Physics © Springer-Verlag 1984 On the Curvature of Piecewise Flat Spaces Jeff Cheeger1'*, Werner Mϋller2, and Robert Schrader3'**'*** 1 Department of Mathematics, SUNY at Stony Brook, Stony Brook, NY 11794, USA 2 Zentralinstitut fur Mathematik, Akademie der Wissenschaften der DDR, Mohrenstrasse 39, DDR-1199 Berlin, German Democratic Republic 3 Institute for Theoretical Physics, SUNY at Stony Brook, Stony Brook, NY 11794, USA Abstract. We consider analogs of the Lipschitz-Killing curvatures of smooth Riemannian manifolds for piecewise flat spaces. In the special case of scalar curvature, the definition is due to T. Regge considerations in this spirit date back to J. Steiner. We show that if a piecewise flat space approximates a smooth space in a suitable sense, then the corresponding curvatures are close in the sense of measures. 0. Introduction Let Xn be a complete metric space and Σ CXn a closed, subset of dimension less than or equal to (n— 1). Assume that Xn\Σ is isometric to a smooth (incomplete) ^-dimensional Riemannian manifold. How should one define the curvature of Xn at points xeΣ, near which the metric need not be smooth and X need not be locally homeomorphic to UCRnΊ In [C, Wi], this question is answered (in seemingly different, but in fact, equivalent ways) for the Lipschitz-Killing curva- tures, and their associated boundary curvatures under the assumption that the metric on Xn is piecewise flat. The precise definitions (which are given in Sect. 2), are formulated in terms of certain "angle defects." For the mean curvature and the scalar curvature they are originally due to Steiner [S] and Regge [R], respectively. -
The Space Meas(X,A)
Chapter 5 The Space Meas(X; A) In this chapter, we will study the properties of the space of finite signed measures on a measurable space (X; A). In particular we will show that with respect to a very natural norm that this space is in fact a Banach space. We will then investigate the nature of this space when X = R and A = B(R). 5.1 The Space Meas(X; A) Definition 5.1.1. Let (X; A) be a measurable space. We let Meas(X; A) = fµ j µ is a finite signed measure on (X; A)g It is clear that Meas(X; A) is a vector space over R. For each µ 2 Meas(X; A) define kµkmeas = jµj(X): Observe that if µ, ν 2 Meas(X; A) and if µ = µ+ − µ− and ν = ν+ − ν− are the respective Jordan decomposiitions, then kµ + νkmeas = jµ + νj(X) = (µ + ν)+(X) + (µ + ν)−(X) ≤ (µ+(X) + ν+(X)) + (µ−(X) + ν−(X)) = (µ+(X) + µ−(X)) + (ν+(X) + ν−(X)) = jµj(X) + jνj(X) = kµkmeas + kνkmeas From here it is easy to see that (Meas(X; A); k · kmeas) is a normed linear space. Theorem 5.1.2. Let (X; A) be a measurable space. Then (Meas(X; A); k · kmeas) is Banach space. Proof. Let fµng be a Cauchy sequence in (Meas(X; A); k · kmeas). Let E 2 A. Since jµn(E) − µm(E)j ≤ jµn − µmj(E) ≤ kµn − µmkmeas we see that fµn(E)g is also Cauchy in R. We define for E 2 A, µ(E) = lim µn(E): n!1 72 Note that the convergence above is actually uniform on A. -
An Extension of Wiener Integration with the Use of Operator Theory
AN EXTENSION OF WIENER INTEGRATION WITH THE USE OF OPERATOR THEORY PALLE E. T. JORGENSEN AND MYUNG-SIN SONG Abstract. With the use of tensor product of Hilbert space, and a diagonal- ization procedure from operator theory, we derive an approximation formula for a general class of stochastic integrals. Further we establish a generalized Fourier expansion for these stochastic integrals. In our extension, we circum- vent some of the limitations of the more widely used stochastic integral due to Wiener and Ito, i.e., stochastic integration with respect to Brownian motion. Finally we discuss the connection between the two approaches, as well as a priori estimates and applications. Contents 1. Introduction 1 2. Notation and Definitions 2 3. Statement of the Main Theorem 3 4. An Application 4 5. Proof of Theorem 3.1 5 5.1. Part A 6 5.2. Part B 7 6. Entropy: Optimal Bases 8 Acknowledgment 12 References 12 1. Introduction arXiv:0901.0195v1 [math-ph] 1 Jan 2009 Recently there has been increase in the number of applications of stochastic inte- gration and stochastic differential equations (SDEs). In addition to the traditional applications in physics and dynamics, stochastic processes have found uses in such areas as option pricing in finance, filtering in signal processing, computations bi- ological models. This fact suggests a need for a widening of the more traditional approach centered about Brownian motion B(t) and Wiener’s integral. Since SDEs are solved with the use of stochastic integrals, we will focus here on integration with respect to a wider class of stochastic processes than has previously been considered. -
Radon-Nikodym Theorem and Its Applications
Radon-Nikodym Theorem And Its Applications Li Tao PB13001106 Abstraction: Radon-Nikodym theorem is one of the most important result in real analy- sis. In this paper, I will introduce the background of this problem and some basic definitions at first. Then I will give the proof, as well as several applications of Radon-Nikodym theorem, including conditional expectation, the dual space of Lp and change of measure in stochastic analysis. Keywords: Radon-Nikodym theorem, conditional expectation, dual space of Lp, change of measure 1 Introduction and notations Generally speaking, Radon-Nikodym theorem gives the connection between two measures. The theorem is named after Johann Radon, who proved the theorem for the special case where the underlying space is Rn in 1913, and for Otto Nikodym who proved the general case in 1930. In 1936 Hans Freudenthal further generalized the Radon-Nikodym theorem by proving the Freudenthal spectral theorem, a result in Riesz space theory, which contains the Radon-Nikodym theorem as a special case. However, before we state the theorem, we need some basic definitions. In this lecture, we always assume the background mesurable space is (Ω; F). Definition(Measure) (Ω; F) is a measurable space, a set function µ : F! [0; 1] is a 1 P1 measure if µ(;) = 0 and µ([i=1Ai) = i=1 µ(Ai) for disjoint Ai. Definition(Finite Measure) A measure µ on (Ω; F) is finite if µ(Ω) < 1. Definition(σ-Finite Measure) A measure µ on (Ω; F) is σ-finite if 9An 2 F; n ≥ 1 such that [nAn = Ω and µ(An) < 1. -
Markov Processes and Martingale Generalisations on Riesz Spaces
Markov Processes and Martingale Generalisations on Riesz Spaces by Jessica Joy Vardy School of Mathematics University of the Witwatersrand, Private Bag-3, Wits-2050, Johannesburg South Africa April 2013 ABSTRACT In a series of papers by Wen-Chi Kuo, Coenraad Labuschagne and Bruce Watson results of martingale theory were generalised to the abstract setting of Riesz spaces. This thesis presents a survey of those results proved and aims to expand upon the work of these authors. In particular, independence results will be considered and these will be used to generalise well known results in the theory of Markov processes to Riesz spaces. Mixingales and quasi-martingales will be translated to the Riesz space setting. PREFACE Sections of this PhD. Thesis have been submitted for publication. They are as follows: Chapter3, Section 3.2 and Chapter4, Section 4.3. Chapter5, Section 5.2. Chapter6, Section 6.2. ACKNOWLEDGMENTS I would like to acknowledge my supervisor Prof. Bruce A. Watson for his generosity and support during the course of my PhD. DECLARATION I declare that this thesis is my own, unaided work. It is being submitted for the Degree of Doctor of Philosophy in the University of the Witwatersrand, Johannesburg. This thesis was converted from a dissertation submitted to- wards the Degree of Master of Science, under rule 12.2(b). As such, a portion of this work (Chapters 2 - 4) has been examined prior. Jessica Joy Vardy Signed on this the 24th day of April 2013, at Johannesburg, South Africa. Contents Table of Contents vi 1 Introduction1 2 Preliminaries4 2.1 Classical Stochastic Processes......................4 2.1.1 Conditioning...........................5 2.1.2 Some Measure Theory......................8 2.1.3 Properties of Conditional Expectations.............9 2.2 Riesz Spaces............................... -
Hyperbolic Valued Signed Measure
International Journal of Mathematics Trends and Technology (IJMTT) – Volume 55 Number 7 - March 2018 Hyperbolic Valued Signed Measure Chinmay Ghosh¹, Sanjoy Biswas² , Taha Yasin³ ¹Kazi Nazrul University, Nazrul Road, Asansol-713340, Paschim Bardhaman, West Bengal, India. 2,3Guru Nanak Institute of Technology, 157/F, Nilgunj Road, Panihati, Sodepur, Kolkata-700114, West Bengal, India. Abstract: In this article we introduced two symbols and as extended hyperbolic numbers and modified D D the definition of hyperbolic valued measure. Also we defined hyperbolic valued signed measure and proved some theorems on it. AMS Subject Classification (2010) : 28A12. Keywords: Hyperbolic numbers, hyperbolic conjugation, idempotent representation, hyperbolic modulus, D- bounded, D-Cauchy sequence, D-valued measure, D-measure space, D-valued signed measure. I. INTRODUCTION Albert Einstein gave the theory of special relativity at the beginning of the 20th Century which is based on Lorentzian geometry. The hyperbolic numbers (or duplex numbers or perplex numbers), put Lorentzian geometry to an analogous mathematical concept with Euclidean geometry. The hyperbolic numbers play a similar role in the Lorentzian plane that the complex numbers do in the Euclidean plane. Different structures of these numbers can understood well in [2],[3].[5],[6],[7],and [9]. D. Alpay et. al. [1] gave an idea of hyperbolic valued probabilities. This idea motivate to construct hyperbolic valued measure. Before the present work R. Kumar et. al. [4] had given the similar concept which is modified here by introducing two symbols and as extended hyperbolic numbers. Also the concept of hyperbolic valued signed measure is introduced and the hyperbolic version of Hahn and Jordan decomposition theorems are proved in this article.