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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). -
Three Problems of the Theory of Choice on Random Sets
DIVISION OF THE HUMANITIES AND SOCIAL SCIENCES CALIFORNIA INSTITUTE OF TECHNOLOGY PASADENA, CALIFORNIA 91125 THREE PROBLEMS OF THE THEORY OF CHOICE ON RANDOM SETS B. A. Berezovskiy,* Yu. M. Baryshnikov, A. Gnedin V. Institute of Control Sciences Academy of Sciences of the U.S.S.R, Moscow *Guest of the California Institute of Technology SOCIAL SCIENCE WORKING PAPER 661 December 1987 THREE PROBLEMS OF THE THEORY OF CHOICE ON RANDOM SETS B. A. Berezovskiy, Yu. M. Baryshnikov, A. V. Gnedin Institute of Control Sciences Academy of Sciences of the U.S.S.R., Moscow ABSTRACT This paper discusses three problems which are united not only by the common topic of research stated in thetitle, but also by a somewhat surprising interlacing of the methods and techniques used. In the first problem, an attempt is made to resolve a very unpleasant metaproblem arising in general choice theory: why theconditions of rationality are not really necessary or, in other words, why in every-day life we are quite satisfied with choice methods which are far from being ideal. The answer, substantiated by a number of results, is as follows: situations in which the choice function "misbehaves" are very seldom met in large presentations. the second problem, an overview of our studies is given on the problem of statistical propertiesIn of choice. One of themost astonishing phenomenon found when we deviate from scalar extremal choice functions is in stable multiplicity of choice. If our presentation is random, then a random number of alternatives is chosen in it. But how many? The answer isn't trival, and may be sought in many differentdirections. -
Gsm076-Endmatter.Pdf
http://dx.doi.org/10.1090/gsm/076 Measur e Theor y an d Integratio n This page intentionally left blank Measur e Theor y an d Integratio n Michael E.Taylor Graduate Studies in Mathematics Volume 76 M^^t| American Mathematical Society ^MMOT Providence, Rhode Island Editorial Board David Cox Walter Craig Nikolai Ivanov Steven G. Krantz David Saltman (Chair) 2000 Mathematics Subject Classification. Primary 28-01. For additional information and updates on this book, visit www.ams.org/bookpages/gsm-76 Library of Congress Cataloging-in-Publication Data Taylor, Michael Eugene, 1946- Measure theory and integration / Michael E. Taylor. p. cm. — (Graduate studies in mathematics, ISSN 1065-7339 ; v. 76) Includes bibliographical references. ISBN-13: 978-0-8218-4180-8 1. Measure theory. 2. Riemann integrals. 3. Convergence. 4. Probabilities. I. Title. II. Series. QA312.T387 2006 515/.42—dc22 2006045635 Copying and reprinting. Individual readers of this publication, and nonprofit libraries acting for them, are permitted to make fair use of the material, such as to copy a chapter for use in teaching or research. Permission is granted to quote brief passages from this publication in reviews, provided the customary acknowledgment of the source is given. Republication, systematic copying, or multiple reproduction of any material in this publication is permitted only under license from the American Mathematical Society. Requests for such permission should be addressed to the Acquisitions Department, American Mathematical Society, 201 Charles Street, Providence, Rhode Island 02904-2294, USA. Requests can also be made by e-mail to [email protected]. © 2006 by the American Mathematical Society. -
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. -
The Halász-Székely Barycenter
THE HALÁSZ–SZÉKELY BARYCENTER JAIRO BOCHI, GODOFREDO IOMMI, AND MARIO PONCE Abstract. We introduce a notion of barycenter of a probability measure re- lated to the symmetric mean of a collection of nonnegative real numbers. Our definition is inspired by the work of Halász and Székely, who in 1976 proved a law of large numbers for symmetric means. We study analytic properties of this Halász–Székely barycenter. We establish fundamental inequalities that relate the symmetric mean of a list of nonnegative real numbers with the barycenter of the measure uniformly supported on these points. As consequence, we go on to establish an ergodic theorem stating that the symmetric means of a se- quence of dynamical observations converges to the Halász–Székely barycenter of the corresponding distribution. 1. Introduction Means have fascinated man for a long time. Ancient Greeks knew the arith- metic, geometric, and harmonic means of two positive numbers (which they may have learned from the Babylonians); they also studied other types of means that can be defined using proportions: see [He, pp. 85–89]. Newton and Maclaurin en- countered the symmetric means (more about them later). Huygens introduced the notion of expected value and Jacob Bernoulli proved the first rigorous version of the law of large numbers: see [Mai, pp. 51, 73]. Gauss and Lagrange exploited the connection between the arithmetico-geometric mean and elliptic functions: see [BB]. Kolmogorov and other authors considered means from an axiomatic point of view and determined when a mean is arithmetic under a change of coordinates (i.e. quasiarithmetic): see [HLP, p. -
TOPIC 0 Introduction
TOPIC 0 Introduction 1 Review Course: Math 535 (http://www.math.wisc.edu/~roch/mmids/) - Mathematical Methods in Data Science (MMiDS) Author: Sebastien Roch (http://www.math.wisc.edu/~roch/), Department of Mathematics, University of Wisconsin-Madison Updated: Sep 21, 2020 Copyright: © 2020 Sebastien Roch We first review a few basic concepts. 1.1 Vectors and norms For a vector ⎡ 푥 ⎤ ⎢ 1 ⎥ 푥 ⎢ 2 ⎥ 푑 퐱 = ⎢ ⎥ ∈ ℝ ⎢ ⋮ ⎥ ⎣ 푥푑 ⎦ the Euclidean norm of 퐱 is defined as ‾‾푑‾‾‾‾ ‖퐱‖ = 푥2 = √퐱‾‾푇‾퐱‾ = ‾⟨퐱‾‾, ‾퐱‾⟩ 2 ∑ 푖 √ ⎷푖=1 푇 where 퐱 denotes the transpose of 퐱 (seen as a single-column matrix) and 푑 ⟨퐮, 퐯⟩ = 푢 푣 ∑ 푖 푖 푖=1 is the inner product (https://en.wikipedia.org/wiki/Inner_product_space) of 퐮 and 퐯. This is also known as the 2- norm. More generally, for 푝 ≥ 1, the 푝-norm (https://en.wikipedia.org/wiki/Lp_space#The_p- norm_in_countably_infinite_dimensions_and_ℓ_p_spaces) of 퐱 is given by 푑 1/푝 ‖퐱‖ = |푥 |푝 . 푝 (∑ 푖 ) 푖=1 Here (https://commons.wikimedia.org/wiki/File:Lp_space_animation.gif#/media/File:Lp_space_animation.gif) is a nice visualization of the unit ball, that is, the set {퐱 : ‖푥‖푝 ≤ 1}, under varying 푝. There exist many more norms. Formally: 푑 푑 Definition (Norm): A norm is a function ℓ from ℝ to ℝ+ that satisfies for all 푎 ∈ ℝ, 퐮, 퐯 ∈ ℝ (Homogeneity): ℓ(푎퐮) = |푎|ℓ(퐮) (Triangle inequality): ℓ(퐮 + 퐯) ≤ ℓ(퐮) + ℓ(퐯) (Point-separating): ℓ(푢) = 0 implies 퐮 = 0. ⊲ The triangle inequality for the 2-norm follows (https://en.wikipedia.org/wiki/Cauchy– Schwarz_inequality#Analysis) from the Cauchy–Schwarz inequality (https://en.wikipedia.org/wiki/Cauchy– Schwarz_inequality). -
Continuous Probability Distributions Continuous Probability Distributions – a Guide for Teachers (Years 11–12)
1 Supporting Australian Mathematics Project 2 3 4 5 6 12 11 7 10 8 9 A guide for teachers – Years 11 and 12 Probability and statistics: Module 21 Continuous probability distributions Continuous probability distributions – A guide for teachers (Years 11–12) Professor Ian Gordon, University of Melbourne Editor: Dr Jane Pitkethly, La Trobe University Illustrations and web design: Catherine Tan, Michael Shaw Full bibliographic details are available from Education Services Australia. Published by Education Services Australia PO Box 177 Carlton South Vic 3053 Australia Tel: (03) 9207 9600 Fax: (03) 9910 9800 Email: [email protected] Website: www.esa.edu.au © 2013 Education Services Australia Ltd, except where indicated otherwise. You may copy, distribute and adapt this material free of charge for non-commercial educational purposes, provided you retain all copyright notices and acknowledgements. This publication is funded by the Australian Government Department of Education, Employment and Workplace Relations. Supporting Australian Mathematics Project Australian Mathematical Sciences Institute Building 161 The University of Melbourne VIC 3010 Email: [email protected] Website: www.amsi.org.au Assumed knowledge ..................................... 4 Motivation ........................................... 4 Content ............................................. 5 Continuous random variables: basic ideas ....................... 5 Cumulative distribution functions ............................ 6 Probability density functions .............................. -
Probability with Engineering Applications ECE 313 Course Notes
Probability with Engineering Applications ECE 313 Course Notes Bruce Hajek Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign January 2017 c 2017 by Bruce Hajek All rights reserved. Permission is hereby given to freely print and circulate copies of these notes so long as the notes are left intact and not reproduced for commercial purposes. Email to [email protected], pointing out errors or hard to understand passages or providing comments, is welcome. Contents 1 Foundations 3 1.1 Embracing uncertainty . .3 1.2 Axioms of probability . .6 1.3 Calculating the size of various sets . 10 1.4 Probability experiments with equally likely outcomes . 13 1.5 Sample spaces with infinite cardinality . 15 1.6 Short Answer Questions . 20 1.7 Problems . 21 2 Discrete-type random variables 25 2.1 Random variables and probability mass functions . 25 2.2 The mean and variance of a random variable . 27 2.3 Conditional probabilities . 32 2.4 Independence and the binomial distribution . 34 2.4.1 Mutually independent events . 34 2.4.2 Independent random variables (of discrete-type) . 36 2.4.3 Bernoulli distribution . 37 2.4.4 Binomial distribution . 38 2.5 Geometric distribution . 41 2.6 Bernoulli process and the negative binomial distribution . 43 2.7 The Poisson distribution{a limit of binomial distributions . 45 2.8 Maximum likelihood parameter estimation . 47 2.9 Markov and Chebychev inequalities and confidence intervals . 50 2.10 The law of total probability, and Bayes formula . 53 2.11 Binary hypothesis testing with discrete-type observations . -
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. -
HARMONIC ANALYSIS 1. Maximal Function for a Locally Integrable
HARMONIC ANALYSIS PIOTR HAJLASZ 1. Maximal function 1 n For a locally integrable function f 2 Lloc(R ) the Hardy-Littlewood max- imal function is defined by Z n Mf(x) = sup jf(y)j dy; x 2 R : r>0 B(x;r) The operator M is not linear but it is subadditive. We say that an operator T from a space of measurable functions into a space of measurable functions is subadditive if jT (f1 + f2)(x)j ≤ jT f1(x)j + jT f2(x)j a.e. and jT (kf)(x)j = jkjjT f(x)j for k 2 C. The following integrability result, known also as the maximal theorem, plays a fundamental role in many areas of mathematical analysis. p n Theorem 1.1 (Hardy-Littlewood-Wiener). If f 2 L (R ), 1 ≤ p ≤ 1, then Mf < 1 a.e. Moreover 1 n (a) For f 2 L (R ) 5n Z (1.1) jfx : Mf(x) > tgj ≤ jfj for all t > 0. t Rn p n p n (b) If f 2 L (R ), 1 < p ≤ 1, then Mf 2 L (R ) and p 1=p kMfk ≤ 2 · 5n=p kfk for 1 < p < 1, p p − 1 p kMfk1 ≤ kfk1 : Date: March 28, 2012. 1 2 PIOTR HAJLASZ The estimate (1.1) is called weak type estimate. 1 n 1 n Note that if f 2 L (R ) is a nonzero function, then Mf 62 L (R ). Indeed, R if λ = B(0;R) jfj > 0, then for jxj > R we have Z λ Mf(x) ≥ jfj ≥ n ; B(x;R+jxj) !n(R + jxj) n and the function on the right hand side is not integrable on R . -
Probabilistic Models for Shapes As Continuous Curves
J Math Imaging Vis (2009) 33: 39–65 DOI 10.1007/s10851-008-0104-3 Probabilistic Models for Shapes as Continuous Curves Jeong-Gyoo Kim · J. Alison Noble · J. Michael Brady Published online: 23 July 2008 © Springer Science+Business Media, LLC 2008 Abstract We develop new shape models by defining a stan- 1 Introduction dard shape from which we can explain shape deformation and variability. Currently, planar shapes are modelled using Our work contributes to shape analysis, particularly in med- a function space, which is applied to data extracted from ical image analysis, by defining a standard representation images. We regard a shape as a continuous curve and identi- of shape. Specifically we develop new mathematical mod- fied on the Wiener measure space whereas previous methods els of planar shapes. Our aim is to develop a standard rep- have primarily used sparse sets of landmarks expressed in a resentation of anatomical or biological shapes that can be Euclidean space. The average of a sample set of shapes is used to explain shape variations, whether in normal subjects defined using measurable functions which treat the Wiener or abnormalities due, for example, to disease. In addition, measure as varying Gaussians. Various types of invariance we propose a quasi-score that measures global deformation of our formulation of an average are examined in regard to and provides a generic way to compare statistical methods practical applications of it. The average is examined with of shape analysis. relation to a Fréchet mean in order to establish its valid- As D’Arcy Thompson pointed in [50], there is an impor- ity. -
Two Notes on Financial Mathematics
TWO NOTES ON FINANCIAL MATHEMATICS Daniel Dufresne Centre for Actuarial Studies University of Melbourne The two notes which follow are aimed at students of financial mathematics or actuarial science. The first one, “A note on correlation in mean variance portfolio theory” is an elementary discussion of how negative correlation coefficients can be, given any set of n random variables. The second one, “Two essential formulas in actuarial science and financial mathematics,” gives a more or less self-contained introduction to Jensen’s inequality and to another formula, which I have decided to call the “survival function expectation formula.” The latter allows one to write the expectation of a function of a random variable as an integral involving the complement of its cumulative distribution function. A NOTE ON CORRELATION IN MEAN VARIANCE PORTFOLIO THEORY Daniel Dufresne Centre for Actuarial Studies University of Melbourne Abstract This note is aimed at students and others interested in mean variance portfolio theory. Negative correlations are desirable in portfolio selection, as they decrease risk. It is shown that there is a mathematical limit to how negative correlations can be among a given number of securities. In particular, in an “average correlation model” (where the correlation coefficient between different securities is constant) the correlation has to be at least as large as −1/(n − 1), n being the number of securities. Keywords: Mean-variance portfolio theory; average correlation mod- els 1. INTRODUCTION One of the more technical points encountered when teaching the basics of mean variance portfolio theory is the restrictions which apply on the covariance matrix of the securities’ returns.