Product Integration, Its History and Applications, 2007 (Engl.) Antonín Slavík
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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). -
Arxiv:1207.1472V2 [Math.CV]
SOME SIMPLIFICATIONS IN THE PRESENTATIONS OF COMPLEX POWER SERIES AND UNORDERED SUMS OSWALDO RIO BRANCO DE OLIVEIRA Abstract. This text provides very easy and short proofs of some basic prop- erties of complex power series (addition, subtraction, multiplication, division, rearrangement, composition, differentiation, uniqueness, Taylor’s series, Prin- ciple of Identity, Principle of Isolated Zeros, and Binomial Series). This is done by simplifying the usual presentation of unordered sums of a (countable) family of complex numbers. All the proofs avoid formal power series, double series, iterated series, partial series, asymptotic arguments, complex integra- tion theory, and uniform continuity. The use of function continuity as well as epsilons and deltas is kept to a mininum. Mathematics Subject Classification: 30B10, 40B05, 40C15, 40-01, 97I30, 97I80 Key words and phrases: Power Series, Multiple Sequences, Series, Summability, Complex Analysis, Functions of a Complex Variable. Contents 1. Introduction 1 2. Preliminaries 2 3. Absolutely Convergent Series and Commutativity 3 4. Unordered Countable Sums and Commutativity 5 5. Unordered Countable Sums and Associativity. 9 6. Sum of a Double Sequence and The Cauchy Product 10 7. Power Series - Algebraic Properties 11 8. Power Series - Analytic Properties 14 References 17 arXiv:1207.1472v2 [math.CV] 27 Jul 2012 1. Introduction The objective of this work is to provide a simplification of the theory of un- ordered sums of a family of complex numbers (in particular, for a countable family of complex numbers) as well as very easy proofs of basic operations and properties concerning complex power series, such as addition, scalar multiplication, multipli- cation, division, rearrangement, composition, differentiation (see Apostol [2] and Vyborny [21]), Taylor’s formula, principle of isolated zeros, uniqueness, principle of identity, and binomial series. -
Formal Power Series - Wikipedia, the Free Encyclopedia
Formal power series - Wikipedia, the free encyclopedia http://en.wikipedia.org/wiki/Formal_power_series Formal power series From Wikipedia, the free encyclopedia In mathematics, formal power series are a generalization of polynomials as formal objects, where the number of terms is allowed to be infinite; this implies giving up the possibility to substitute arbitrary values for indeterminates. This perspective contrasts with that of power series, whose variables designate numerical values, and which series therefore only have a definite value if convergence can be established. Formal power series are often used merely to represent the whole collection of their coefficients. In combinatorics, they provide representations of numerical sequences and of multisets, and for instance allow giving concise expressions for recursively defined sequences regardless of whether the recursion can be explicitly solved; this is known as the method of generating functions. Contents 1 Introduction 2 The ring of formal power series 2.1 Definition of the formal power series ring 2.1.1 Ring structure 2.1.2 Topological structure 2.1.3 Alternative topologies 2.2 Universal property 3 Operations on formal power series 3.1 Multiplying series 3.2 Power series raised to powers 3.3 Inverting series 3.4 Dividing series 3.5 Extracting coefficients 3.6 Composition of series 3.6.1 Example 3.7 Composition inverse 3.8 Formal differentiation of series 4 Properties 4.1 Algebraic properties of the formal power series ring 4.2 Topological properties of the formal power series -
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. -
Math 263A Notes: Algebraic Combinatorics and Symmetric Functions
MATH 263A NOTES: ALGEBRAIC COMBINATORICS AND SYMMETRIC FUNCTIONS AARON LANDESMAN CONTENTS 1. Introduction 4 2. 10/26/16 5 2.1. Logistics 5 2.2. Overview 5 2.3. Down to Math 5 2.4. Partitions 6 2.5. Partial Orders 7 2.6. Monomial Symmetric Functions 7 2.7. Elementary symmetric functions 8 2.8. Course Outline 8 3. 9/28/16 9 3.1. Elementary symmetric functions eλ 9 3.2. Homogeneous symmetric functions, hλ 10 3.3. Power sums pλ 12 4. 9/30/16 14 5. 10/3/16 20 5.1. Expected Number of Fixed Points 20 5.2. Random Matrix Groups 22 5.3. Schur Functions 23 6. 10/5/16 24 6.1. Review 24 6.2. Schur Basis 24 6.3. Hall Inner product 27 7. 10/7/16 29 7.1. Basic properties of the Cauchy product 29 7.2. Discussion of the Cauchy product and related formulas 30 8. 10/10/16 32 8.1. Finishing up last class 32 8.2. Skew-Schur Functions 33 8.3. Jacobi-Trudi 36 9. 10/12/16 37 1 2 AARON LANDESMAN 9.1. Eigenvalues of unitary matrices 37 9.2. Application 39 9.3. Strong Szego limit theorem 40 10. 10/14/16 41 10.1. Background on Tableau 43 10.2. KOSKA Numbers 44 11. 10/17/16 45 11.1. Relations of skew-Schur functions to other fields 45 11.2. Characters of the symmetric group 46 12. 10/19/16 49 13. 10/21/16 55 13.1. -
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. -
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. -
Complex Function Theory
Complex Function Theory Second Edition Donald Sarason AMERICAN MATHEMATICAL SOCIETY http://dx.doi.org/10.1090/mbk/049 Complex Function Theory Second Edition Donald Sarason AMERICAN MATHEMATICAL SOCIETY 2000 Mathematics Subject Classification. Primary 30–01. Front Cover: The figure on the front cover is courtesy of Andrew D. Hwang. The Hindustan Book Agency has the rights to distribute this book in India, Bangladesh, Bhutan, Nepal, Pakistan, Sri Lanka, and the Maldives. For additional information and updates on this book, visit www.ams.org/bookpages/mbk-49 Library of Congress Cataloging-in-Publication Data Sarason, Donald. Complex function theory / Donald Sarason. — 2nd ed. p. cm. Includes index. ISBN-13: 978-0-8218-4428-1 (alk. paper) ISBN-10: 0-8218-4428-8 (alk. paper) 1. Functions of complex variables. I. Title. QA331.7.S27 2007 515.9—dc22 2007060552 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]. c 2007 by the American Mathematical Society. All rights reserved. -
A Certain Integral-Recurrence Equation with Discrete-Continuous Auto-Convolution
ARCHIVUM MATHEMATICUM (BRNO) Tomus 47 (2011), 245–250 A CERTAIN INTEGRAL-RECURRENCE EQUATION WITH DISCRETE-CONTINUOUS AUTO-CONVOLUTION Mircea I. Cîrnu Abstract. Laplace transform and some of the author’s previous results about first order differential-recurrence equations with discrete auto-convolution are used to solve a new type of non-linear quadratic integral equation. This paper continues the author’s work from other articles in which are considered and solved new types of algebraic-differential or integral equations. 1. Introduction In the earlier paper [4], N. M. Flaisher solved by Fourier transform method a second order differential-recurrence equation. The present author used in [2] the Laplace transform to derive Newton’s formulas about the sums of powers of the roots of a polynomial. In this paper, the Laplace transform will be used to solve an integral-recurrence equation on semi-axis, with discrete-continuous auto-convolution of its unknowns. Namely, applying the Laplace transform on considered equation, we obtain for the transforms of unknowns a first order differential-recurrence equation with discrete auto-convolution of the type studied in [3] and [1]. Using for this equation the general theory given in [3], we find the transforms of unknowns in convenient assumptions, the solutions of the initial equation being obtained by inverse Laplace transform. 2. Convolution products Two convolution products have been imposed over the time. The first, in continuous-variable case, is the bilateral convolution of two integrable functions u(x) and v(x) on real axis, given by formula Z ∞ u(x) ? v(x) = u(t)v(x − t) dt , −∞ 2010 Mathematics Subject Classification: primary 45G10; secondary 44A10. -
M 3001 Assignment 5: Solution
M 3001 ASSIGNMENT 5: SOLUTION Due in class: Wednesday, Feb. 16, 2011 Name M.U.N. Number P∞ P∞ P∞ 1. Suppose n=1 an = ∞. Let n=1 bn be a regrouping of n=1 an formed by inserting paren- P∞ theses. Prove that n=1 bn = ∞. P∞ P∞ Solution. Since n=1 bn is a regrouping of n=1 an, there is an increasing function φ : N → N such that bn = aφ(n). Note that for any M > 0 there is an N ∈ N such that n > N implies Pn Pn Pφ(n) Pn P∞ k=1 ak > M. So k=1 bk = k=1 ak ≥ k=1 ak > M. That is to say, n=1 bn = ∞. P∞ P∞ P∞ 2. If n=1 an converges absolutely and n=1 bn is a rearrangement of n=1 an, prove that P∞ P∞ n=1 |bn| = n=1 |an|. P∞ P∞ P∞ Solution. Since n=1 |an| converges and n=1 |bn| is a rearrangement of n=1 |an|. By the P∞ P∞ theorem on series rearrangement proved in Section 2.3, n=1 |bn| equals n=1 |an|. 3. Give an example showing that a rearrangement of a divergent series may diverge or converge. Solution. Since a series is a rearrangement of itself, a divergent series is its own divergent P∞ rearrangement. Suppose n=1 an is conditionally convergent. Then there is an arrangement P∞ P∞ P∞ n=1 bn = ∞ of n=1 an. In fact, let p1, p2, ... be the nonnegative terms of n=1 an in the P∞ order in which they occur; let −q1, −q2, .. -
Generating Functions
MATH 324 Summer 2012 Elementary Number Theory Notes on Generating Functions Generating Functions In this note we will discuss the ordinary generating function of a sequence of real numbers, and then find the generating function for some sequences, including the sequence of Mersenne numbers and the sequence of Fibonacci numbers. Definition. Given a sequence ak k≥0 of real numbers, the ordinary generating function, or generating f g function, of the sequence ak k≥0 is defined to be f g 1 k 2 G(x) = akx = a0 + a1x + a2x + : ( ) k=0 · · · ∗ X The sum is finite if the sequence is finite, and infinite if the sequence is infinite. In the latter case, we will think of ( ) as either a power series with x chosen so that the sum converges, or as a formal power series, where we ∗are not concerned with convergence. Example 1. Suppose that n is a fixed positive integer and n ak = k for k = 0; 1; : : : ; n: From the binomial theorem we have n n n n n n (1 + x) = + x + x2 + + x ; 0 1 2 · · · n n and the generating function for the sequence ak k is f g =0 n G(x) = (1 + x) : Note: The advantage of what we have done is that we have expressed G(x) in a very simple encoded form. If we know this encoded form for G(x); it is now easy to derive ak by decoding or expanding G(x) as a k power series and finding the coefficient of x : Often we will be able to find G(x) without knowing ak; and then be able to solve for ak by expanding G(x): Example 2. -
Product Integral Techniques Are Used to Represent a Solution U to the Abstract System: £/I2(X, Y) = AU(X, Y); U(X, 0) = P = U(0, Y)
TRANSACTIONS OF THE AMERICAN MATHEMATICAL SOCIETY Volume 209, 1975 PRODUCTINTEGRAL TECHNIQUES FOR ABSTRACT HYPERBOLICPARTIAL DIFFERENTIALEQUATIONS^ ) BY J. W. SPELLMANN ABSTRACT. Explicit and implicit product integral techniques are used to represent a solution U to the abstract system: £/i2(x, y) = AU(x, y); U(x, 0) = p = U(0, y). The coefficient A is a closed linear transformation defined on a dense subspace D(A) of the Banach space X and the point p in D(A) satisfies the condition that WA'pW< S'(i\)3/2 for all integers i > 0 and some S > 0. The im- plicit technique is developed under the additional assumption that A generates a strongly continuous semigroup of bounded linear transformations on X. Both methods provide representations for the Jq Bessel function. I. Introduction. Let X denote a Banach space and A denote a closed linear (and, in general, unbounded) transformation defined on a dense subspace D(A) of X. A point p in X is said to satisfy condition (I) if and only if (a) p is in the domain of A ' for all positive integers i and (b) there is a positive number S such that lU'pll < S*(i!)3/2 for all integers i > 0. In this paper two product integral type techniques are used to represent a solu- tion to (II) UX2(x,y) = AU(x, y), U(x, 0) = p = U(0,y), where U is a function from the unit disk [0, 1] x [0, 1] to D(A) and p is a point in X satisfying condition (I).