DOCSLIB.ORG

Markov Processes and the Product-Integral

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Markov Processes and the Product-Integral Appendix A Markov processes and the product-integral A main purpose of this book is to show how the theory of stochastic processes fits naturally into the more applied framework of event history analysis. In particular, the tools and ideas of martingale and Markov process theory are ubiquitous in an in- tegrated approach. In spite of the close relationship, most presentations found in the literature do not acknowledge this; standard presentations of event history analysis rarely say much about Markov processes, and vice versa. Our main text incorporates many applications of martingale theory and counting processes and this appendix will not say much about that. The fundamental con- nections between hazard, survival, Markov processes, the Kolmogorov equations, and the product-integral are also at the heart of many models covered in our book, but a full discussion is too much to include in the main text. Since these topics are hard to find as a comprehensive text, they are presented in this appendix. The results are particularly useful for understanding the connection between the Nelson- Aalen and Kaplan-Meier estimators (Chapter 3) and for multivariate survival data and competing risk (Section 3.4). This appendix deals mostly with the mathemati- cal relationships; the parallel results used in estimation and asymptotics are in the main text. For extensions of multistate Markov models to semi-Markov models, the reader is referred to Huzurbazar (1999, 2005). We also cover introductory ideas for diffusion processes, stochastic differential equations, and certain stationary processes with independent increments, known as Lévy processes. These models are used extensively in the final chapters of the book (Chapters 10 and 11). The material in the appendix is meant to be self-contained, but a reader with no background in stochastic processes will find it useful (or even necessary!) to consult standard texts such as Karlin and Taylor (1975, 1981, 1998), Allen (2003), and Cox and Miller (1965). 457 458 A Markov processes and the product-integral A.1 Hazard, survival, and the product-integral Let T ≥ 0 be a random survival time with survival function S(t)=P(T > t).Itis common to assume that the survival function S(t) is absolutely continuous, and let us do so for the moment. Let f (t) be the density of T . The standard definition of the hazard rate α(t) of T is 1 f (t) α(t)= lim P(t ≤ T < t + Δt | T ≥ t)= , (A.1) Δt→0 Δt S(t) where dt is “infinitesimally small”, that is, the probability of something happening in the immediate future conditional on survival until time t.Thenα is obtainable from S by −S(t) α(t)= . (A.2) S(t) Inversely, when α is available S solves the differential equation S(t)=−α(t)S(t), which leads to t S(t)=exp(− α(s)ds)=exp(−A(t)), (A.3) 0 ( )= t α( ) where A t 0 s ds is the cumulative hazard rate. Note that T does not have to be ( = ∞) > ∞ ( ) < ∞ α( ) < ∞ finite; if P T 0then 0 f s ds 1and 0 s ds , which is sometimes referred to as a defective survival distribution. We will now see how (A.2)and(A.3) may be generalized to arbitrary distribu- tions, which need neither be absolutely continuous nor discrete. Such generaliza- tions, in particular the product-integral, which generalizes (A.3), are useful for a number of reasons: • It enables us to handle both discrete and continuous distributions within a unified framework. • It is the key to understanding the intimate relation between the Kaplan-Meier and Nelson-Aalen estimators (Chapter 3). • It is the basis for the martingale representation of the Kaplan-Meier estimator; cf. Section 3.2.6. • It can readily be extended to Markov processes and used to derive a generaliza- tion of the Kaplan-Meier estimator to multivariate survival data; cf. Section 3.4 and Section A.2.4. We now prepare the ground for the product-integral. When S is not absolutely continuous, it is still right-continuous with limits from the left, that is, a cadlag function. Informally, let dS(t) be the increment of S over the small time interval [t,t +dt),sothat−dS(t)=P(t ≤ T < t +dt).IfT has a density, we have −dS(t)= f (t)dt,andifT has point mass pt in t then −dS(t)=pt, still in a rather informal sense. If we let S(t−) denote the left-hand limits of S(t), the definition (A.1)gives −dS(t) α(t)dt = P(t ≤ T < t + dt | T ≥ t)= , S(t−) A.1 Hazard, survival, and the product-integral 459 and in this form, since S is monotone, it can be integrated on both sides to yield t dS(u) A(t)=− . (A.4) 0 S(u−) The integral on the right-hand side is now well defined as a Stiltjes integral (Rudin, 1976) regardless of whether S is absolutely continuous or not, and we take this as the definition of the cumulative hazard A in general, also when the hazard α(t)=dA(t)/dt itself does not exist. Note that for the absolutely continuous case dS(u)=− f (u)du and S(u−)=S(u), so in this case (A.4) specializes to ( )= t ( )/ ( ) A t 0 f u S u du in agreement with (A.2). On the other hand, for a purely discrete distribution, (A.4) takes the form A(t)=∑u≤t αu, where the discrete hazard αu = −{S(u) − S(u−)}/S(u−)=P(T = u| T ≥ u) is the conditional probability that the event occurs exactly at time u given that it has not occurred earlier. Equation (A.4) expresses the cumulative hazard in terms of the survival function. We also need the inverse representation. In differential form (A.4) may be written dS(t)=−S(t−) dA(t), (A.5) or more formally as an integral equation t S(t)=1 − S(u−)dA(u) (A.6) 0 for S. In the absolutely continuous case this is just an integrated version of (A.2), but the simple solution (A.3) is not valid in general. In fact, the much-studied integral equation (A.6) may serve as an implicit definition of S when given an increasing function A as the cumulative hazard. We will sketch a (slightly simplified) argument of how an expression for the solu- tion S can be found. Consider first the conditional survival function S(v| u)=P(T > v| T > u)=S(v)/S(u), which is the probability of an event occurring later than time v given that it has not yet occurred at time u, v > u. The conditional survival func- tion is an important concept that corresponds more generally to Markov transition probabilities when an individual can move between several different states, not only from “alive” to “dead”. We will discuss the extension in A.2.4. Partition the time interval (0,t] into a number of subintervals 0 = t0 < t1 < t2 < ···< tK = t. To survive from time 0 to time t, an individual needs to survive all the intermediate subintervals. By proper conditioning, we see that S(t)=P(T1 > t1)P(T2 > t2 | T1 > t1)···P(T > tK | T > tK−1) K = ∏ S(tk | tk−1). (A.7) k=1 460 A Markov processes and the product-integral Observe that, from (A.5), we have the approximation S(tk) − S(tk−1) ≈−S(tk−1)(A(tk) − A(tk−1)) (A.8) or S(tk | tk−1) ≈ 1 − (A(tk) − A(tk−1)). Entering this in (A.7), we get K S(t) ≈ ∏ {1 − (A(tk) − A(tk−1))}. (A.9) k=1 One would expect the approximation to improve with increasing number of subin- tervals, and if in this product we let the number K of time intervals increase while their lengths go to zero in a uniform way, the product on the right-hand side will in- deed approach a limit, which is termed the product-integral. In fact, for an arbitrary cadlag function B(t) (of locally bounded variation) the product-integral is defined as K def π{1 + dB(u)} = lim ∏ {1 +(B(tk) − B(tk−1))}, M→0 u≤t k=1 where M = maxk |tk −tk−1| is the length of the longest subinterval. Here the product- integral notation π is used to suggest a limit of finite products ∏, just as the integral is a limit of finite sums ∑. Whereas (A.4) expresses A in terms of S the inverse relation can thus be ex- pressed in terms of the product-integral S(t)=π{1 − dA(u)}. (A.10) u≤t For a purely discrete distribution, this relationship takes the form S(t)=∏(1 − αu). u≤t When A is absolutely continuous we write dA(u)=α(u)du. Using the approxima- tion exp(α(u)du) ≈ 1 − α(u)du, valid for small du, it is seen (informally) that S(t)=π{1 − dA(u)} = π{1 − α(u)du} u≤t u≤t = exp − α(u)du = exp{−A(t)}, u≤t so the product-integral specializes to the well-known relation (A.3). More gener- ally, if we decompose the cumulative hazard into a continuous and a discrete part, that is, A(t)=Ac(t)+Ad(t),whereAc(t) is continuous and Ad(t) is a step function, then the product-integral may be factored as A.2 Markov chains, transition intensities, and the Kolmogorov equations 461 −Ac(t) π{1 − dA(u)} = e ∏{1 − Ad(u)}.
Load more

Recommended publications
  • 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).
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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).
    [Show full text]
  • Product Integration. Its History and Applications
    Product integration. Its history and applications Introduction In: Antonín Slavík (author): Product integration. Its history and applications. (English). Praha: Matfyzpress, 2007. pp. 3–12. Persistent URL: http://dml.cz/dmlcz/401132 Terms of use: © Antonín Slavík Institute of Mathematics of the Czech Academy of Sciences provides access to digitized documents strictly for personal use. Each copy of any part of this document must contain these Terms of use. This document has been digitized, optimized for electronic delivery and stamped with digital signature within the project DML-CZ: The Czech Digital Mathematics Library http://dml.cz Chapter 1 Introduction This chapter starts with a brief look on the prehistory of product integration; the first section summarizes some results concerning ordinary differential equations that were obtained prior the discovery of product integral. The next part provides a motivation for the definition of product integral, and the last two sections describe simple applications of product integration in physics and in probability theory. 1.1 Ordinary differential equations in the 19th century The notion of product integral was introduced by Vito Volterra in connection with the differential equation of the n-th order (n) (n 1) y (x) + p1(x)y − (x) + + p (x)y(x) = q(x). (1.1.1) ··· n Such an equation can be converted (see Example 2.5.5) into a system of n linear differential equations of the first order n yi0(x) = aij(x)yj(x) + bi(x), i = 1, . , n, j=1 X which can be also written in the vector form y0(x) = A(x)y(x) + b(x).
    [Show full text]
  • Richard D. Gill S0ren Johansen Product-Integrals and Counting
    Richard D. Gill S0ren Johansen Product-Integrals and Counting Processes Preprint June 1987 4 Institute of Mathematical Statistics University of Copenhagen ISSN 0902-8846 Richard D. Gill and S~ren Johansen PRODUCT-INTEGRALS AND COUNTING PROCESSES Preprint 1987 No. 4 INSTITUTE OF l1ATHEMATICAL STATISTICS UNIVERSITY OF COPENHAGEN June 1987 Product-integrals and counting processes by Richard D. Gill and SjZ5ren Iohansen Centrum voor Wiskunde en Infonnatica, Institute of Mathematical Statistics, Kruislaan 413 University of Copenhagen, 1098 SI Amsterdam, Universitetsparken 5, Netherlands. 2100 Copenhagen 0, Denmark. June 1987 ABSTRACT. The basic theory of the product-integral TICI +dX) is summarized and applications in probability and statistics are discussed, in particular to non-homogeneous Markov processes, counting process likelihoods and the product-limit estimator. KEy WORDS AND PHRASES. Markov process, multiplicative integral, Volterra integral equation, intensity measure, exponential semimartingale, compact CHadamard) differentiability, survival analysis, product-limit (Kaplan-Meier) estimator. MATHEMATICS CLASSIFICATION. Primary: 60127,62G05; secondary: 60H20, 45D05. June, 1987 Product-integrals and counting processes R. D. Gill & S.lohansen 1. Introduction. Product-integration has a long and respectable history in pure and applied mathematics. At the same time it has many applications in statistics and probability, especially in Markov processes, survival analysis and counting process models. However the product-integral (also known as the multiplicative integral) is almost unknown among statisticians and probabilists, and its properties are continually being rediscovered. We shall try to remedy this situation by collecting together the key facts on matrix product-integration over intervals of the real line, giving self-contained proofs, in a notation which, apart from that of the product integral II(l+dX) itself, should be familiar to our intended audience.
    [Show full text]
  • Lectures on Integration
    Lectures on Integration William G. Faris March 4, 2001 2 Contents 1 The integral: properties 5 1.1 Measurable functions . 5 1.2 Integration . 7 1.3 Convergence theorems . 8 1.4 Measure . 9 1.5 Fubini's theorem . 10 1.6 Problems . 13 2 Function spaces 17 2.1 Spaces of continuous functions . 17 2.2 Lp spaces . 18 2.3 Duality of Lp spaces . 20 2.4 Orlicz spaces . 22 2.5 Problems . 24 3 Probability 27 3.1 Random variables and expectation . 27 3.2 Probability models . 30 3.3 The sample mean . 31 3.4 The sample variance . 32 3.5 Events and probability . 33 3.6 The sample proportion . 34 3.7 Orthogonal projection . 35 3.8 Conditional expectation and probability . 36 3.9 Problems . 38 4 Random walk and martingales 41 4.1 Symmetric simple random walk . 41 4.2 Simple random walk . 43 4.3 Gambler's ruin: fair game . 45 4.4 Gambler's ruin: unfair game . 46 4.5 Martingales . 47 4.6 Problems . 49 3 4 CONTENTS 5 The integral: construction 51 5.1 The Daniell construction . 51 5.2 Product integral and Lebesgue integral . 55 5.3 Image integrals . 58 5.4 Problems . 59 6 Radon integrals 63 6.1 Radon measures . 63 6.2 Lower semicontinuous functions . 65 6.3 Weak* convergence . 66 6.4 The central limit theorem . 67 6.5 Problems . 72 Chapter 1 The integral: properties 1.1 Measurable functions A σ-algebra of measurable functions F is a set of real functions defined on a set X.
    [Show full text]
  • Chapter 4 Linear Differential Operators
    Chapter 4 Linear Differential Operators In this chapter we will begin to take a more sophisticated approach to dif- ferential equations. We will define, with some care, the notion of a linear differential operator, and explore the analogy between such operators and matrices. In particular, we will investigate what is required for a linear dif- ferential operator to have a complete set of eigenfunctions. 4.1 Formal vs. concrete operators We will call the object n n 1 d d − L = p0(x) n + p1(x) n 1 + + pn(x); (4.1) dx dx − · · · which we also write as n n 1 p (x)@ + p (x)@ − + + p (x); (4.2) 0 x 1 x · · · n a formal linear differential operator. The word \formal" refers to the fact that we are not yet worrying about what sort of functions the operator is applied to. 4.1.1 The algebra of formal operators Even though they are not acting on anything in particular, we can still form products of operators. For example if v and w are smooth functions of x we can define the operators @x + v(x) and @x + w(x) and find 2 (@x + v)(@x + w) = @x + w0 + (w + v)@x + vw; (4.3) 111 112 CHAPTER 4. LINEAR DIFFERENTIAL OPERATORS or 2 (@x + w)(@x + v) = @x + v0 + (w + v)@x + vw; (4.4) We see from this example that the operator algebra is not usually commuta- tive. The algebra of formal operators has some deep applications. Consider, for example, the operators L = @2 + q(x) (4.5) − x and 3 P = @x + a(x)@x + @xa(x): (4.6) In the last expression, the combination @xa(x) means “first multiply by a(x), and then differentiate the result," so we could also write @xa = a@x + a0: (4.7) We can now form the commutator [P; L] P L LP .
    [Show full text]
  • Multiplicative Riemann Integration in Normed Rings«
    MULTIPLICATIVE RIEMANN INTEGRATION IN NORMED RINGS« BY P. R. MASANI Table of Contents chapter page I. Introduction. 147 II. Normed rings. Finite products. 151 III. Definition of Riemann integration. 154 IV. Boundedness and the domain of integrability. 161 V. Equivalence of additive and multiplicative integrability. 166 VI. Structure of the class of .R-integrable functions. 170 VII. The Peano s< ies. 173 VIII. Differentiation. 179 APPENDIX I. A lemma concerning real integration. 188 II. An extension of Tannery's theorem. 189 BIBLIOGRAPHY. 191 I. Introduction 1. Purpose of this paper. The purpose of this paper is to develop the the- ory of Riemann product integration of functions on real intervals with values in a normed ring (Banach algebra)(2). Product integration of matrix-valued functions was initiated by V. Vol- terra [l, 2](3) in 1887 in connection with the theory of homogeneous linear differential equations. L. Schlesinger [l, 2] (1931) and G. Rasch [l] (1934) simplified and extended this theory by treating the matrices as linear algebras with normed topologies. In 1937 Garrett Birkhoff [2] remarked that the theory of product integration as developed by Schlesinger was applicable to normed rings(4). He also extended the theory of product integration to the nonlinear case. There is no adequate treatment of the Riemann theory for discontinuous functions. Much remains to be done by way of strengthening the Riemann theory. In this paper we develop the Riemann theory for all integrable func- tions on a real interval with values in a normed ring. This extension is diffi- cult in the infinite-dimensional case, for it is known (cf.
    [Show full text]
  • Product Integration
    Product Integration Richard D. Gill Mathematical Institute, University of Utrecht, Netherlands EURANDOM, Eindhoven, Netherlands August 9, 2001 Abstract This is a brief survey of product-integration for biostatisticians. 1 Product-Integration Product-integration was introduced more than 110 years ago by the Italian mathematician Vito Volterra, as a tool in the solution of a certain class of dif- ferential equations. It was studied intensively by mathematicians for half a century, but finally the subject became unfashionable and lapsed into obscurity. That is a pity, since ideas of product-integration make a very natural appearance in survival analysis, and the development of this subject (in particular, of the Kaplan-Meier estimator) could have been a lot smoother if product-integration had been a familiar topic from the start. The Kaplan-Meier estimator is the product-integral of the Nelson-Aalen estimator of the cumulative hazard func- tion; these two estimators bear the same relation to one another as the actual survival function and the actual cumulative hazard function. There are many other applications of product-integration in survival analysis, for instance in the study of multi-state processes (connected to the theory of Markov processes), and in the theory of partial likelihood. Ordinary integration is a generalisation of summation, and properties of integrals are often easily guessed by thinking of them as sums of very, very many terms (all or most of them being very small). Similarly, product-integration generalises the taking of products; a product integral is a product of many, many terms (all or most of them being very close to the number 1).
    [Show full text]
  • Product Integrals and Pathwise Integration
    Concentrated Advanced Course January 7-8, 1999 and Workshop January 11-13, 1999 on Pro duct Integrals and Pathwise Integration Foreword In the week January 7-13, 1999 a course and a workshop on Product Integrals and Path- wise Integration was held by MaPhySto at the Department of Mathematical Sciences, University of Aarhus. The course and workshop was organized by Ole E. Barndor -Nielsen Aarhus, Denmark, Svend Erik Graversen Aarhus, Denmark and Thomas Mikosch Groningen, The Nether- lands. In this lea et we have gathered the program, the list of participants and the workshop abstracts. The notes for the course app eared as the rst volume of the MaPhySto Lecture Notes series and may be fetched from our web-site www.maphysto.dk; hardcopies may also b e ordered. Contents 1 Course Program 2 2 Workshop Program 3 3 Combined list of participants to the Course and to the Workshop 5 4 Abstracts 12 1 Course Program Thursday January 7 R. Dudley: How should integrals of Stieltjes type be de ned? . 9.30-10.30 Coffee/tea R. Norvaisa: Stochastic integrals . 10.50-11.50 R. Dudley: Lyons' work on obstacles to pathwise It^o integration. 12.00-12.30 Lunch 12.30-13.30 R. Dudley: Di erentiability with respect to p-variation norms I. 13.30-14.15 R. Dudley: Di erentiability with respect to p-variation norms II. 14.20-15.05 Coffee/tea R. Norvaisa: Properties of p-variation I. 15.20-16.00 R. Norvaisa: Properties of p-variation II. 16.15-17.00 Friday January 8 R.
    [Show full text]