DOCSLIB.ORG

Class Notes for Math 921/922: Real Analysis, Instructor Mikil Foss

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Class Notes for Math 921/922: Real Analysis, Instructor Mikil Foss University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Math Department: Class Notes and Learning Materials Mathematics, Department of 2010 Class Notes for Math 921/922: Real Analysis, Instructor Mikil Foss Laura Lynch University of Nebraska-Lincoln, [email protected] Follow this and additional works at: https://digitalcommons.unl.edu/mathclass Part of the Science and Mathematics Education Commons Lynch, Laura, "Class Notes for Math 921/922: Real Analysis, Instructor Mikil Foss" (2010). Math Department: Class Notes and Learning Materials. 4. https://digitalcommons.unl.edu/mathclass/4 This Article is brought to you for free and open access by the Mathematics, Department of at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Math Department: Class Notes and Learning Materials by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Class Notes for Math 921/922: Real Analysis, Instructor Mikil Foss Topics include: Semicontinuity, equicontinuity, absolute continuity, metric spaces, compact spaces, Ascoli’s theorem, Stone Weierstrass theorem, Borel and Lebesque measures, measurable functions, Lebesque integration, convergence theorems, Lp spaces, general measure and integration theory, Radon- Nikodyn theorem, Fubini theorem, Lebesque-Stieltjes integration, Semicontinuity, equicontinuity, absolute continuity, metric spaces, compact spaces, Ascoli’s theorem, Stone Weierstrass theorem, Borel and Lebesque measures, measurable functions, Lebesque integration, convergence theorems, Lp spaces, general measure and integration theory, Radon-Nikodyn theorem, Fubini theorem, Lebesque-Stieltjes integration. Prepared by Laura Lynch, University of Nebraska-Lincoln August 2010 Course Information O±ce Hours: 11:30-1 MWF Optional Meeting Time: Thursdays at 5pm Assessment Homework 6 34pts Midterm 33pts Final 33pts (Dec 13 : 1 ¡ 3pm) 1 Chapter 1 Drawbacks to Riemann Integration 1. Not all bounded functions are Riemann Integrable. 2. All Riemann Integrable functions are bounded. 3. To use the following theorem, we must have f 2 R[a; b]: 1 Theorem 1 (Dominated Convergence Theorem for Riemann Integrals, Arzela). Let ffngn=1 ½ R[a; b] and f 2 R[a; b] be given. Suppose there exists g 2 R[a; b] such that jfn(x)j < g(x) for all x 2 [a; b] and lim fn(x) = f(x) for all n!1 Z b Z b x 2 [a; b]; then lim fn(x)dx = f(x)dx: n!1 a a 8 < p 1 if x = q in lowest terms with 1 · q · n on [0; 1]; Example. De¯ne fn(x) = :0 otherwise: Then fn(x) ! ÂQ\[0;1] =: f: Notice here jfn(x)j < 2 for all x 2 [0; 1] but f is not Riemann Integrable. Thus we can not use the theorem. 4. The space R[a; b] is not complete with respect to many useful metrics. Good Properties of Riemann Integration 1. R[a; b] is a vector space R b 2. The functional f 7! a f(x)dx is linear on R[a; b]: R b 3. a f(x)dx ¸ 0 when f(x) ¸ 0 for all x 2 [a; b]: 4. Theorem 1 holds. 1.1 Measurable and Topological Spaces De¯nition (p 113,119). Let X be a nonempty set. 1. A collection T of subsets of X is called a topology of X if it possesses the following properties: (a) ; 2 T and X 2 T : n n (b) If fUjgj=1 ⊆ T ; then \1 Uj 2 T : (c) If fU®g®2A ⊆ T , then [®2AU® 2 T : 2. If T is a topology on X; then (X; T ) is called a topological space. If T is understood, we may just call X itself a topological space. The members of T are called open sets in X: The complements of open sets in X are called closed sets. 3. If X and Y are topological spaces and f :X ! Y; then f is called continuous if f ¡1(V) is open in X for all V that are open in Y: Examples. 1. If X = R; then f;; Rg is a topology on R: 2. If X = R; then the power set fP(R)g is a topology on R: 3. f;; [a>0f(¡a; a)g; Rg is a topology on R: 4. f;; [a<b2Rf(a; b)g; [a2Rf(¡1; a); (a; 1)g; Rg is a topology on R = R [ f§1g: De¯nition (pg21,25,43). 1. A collection M of subsets of X is called a σ¡algebra if (a) ; 2 M and X 2 M: (b) If E 2 M; then EC 2 M: 1 1 (c) If fEjgj=1 ⊆ M; then [j=1Ej 2 M: 2. If M is a σ¡algebra on X; then (X; M) is called a measurable space. If M is understood, then we may just call X itself a measurable space. The members of M are called measurable sets. 3. If (X; M) and (Y; N ) are measurable spaces, then f :X ! Y is called (M; N )¡measurable or measurable if f ¡1(E) 2 M whenever E 2 N : Examples. 1. (R; f;; Rg) is a σ¡algebra 2. (R; f;; f1g; R n f1g; Rg) is a σ¡algebra 3. (R; fE ⊆ RjE or EC is countableg) is a σ¡algebra 1 1 j¡1 Lemma 1. If fEjgj=1 ½ P(X); then fFjgj=1 ⊆ P(X) de¯ned by Fj = Ej n [k=1Ek is a sequence of mutually disjoint sets 1 1 and [j=1Ej = [j=1Fj: Note. As a result of Lemma 1, we can actually modify part (c) of our de¯nition of a σ¡algebra to say 1 1 (c) If fEjg1 ½ M is a sequence of mutually disjoint sets, then [1 Ej 2 M: Remarks. 1. Property (1a) of our de¯nition for σ¡ algebra could be replaced with \; 2 M or X 2 M:" 1 C 1 1 1 C C 2. If fEjgj=1 ⊆ M; then fEj gj=1 ⊆ M and \j=1Ej = [[j=1Ej ] ⊆ M: 3. If E; F 2 M; then E n F = E \ F C 2 M: Theorem 2. If E is a collection of subsets of X; then there exists a unique smallest σ¡algebra M(E) that contains the members of E: Note: By smallest, we mean any other σ¡algebra will contain all the sets in M(E): Proof. Let ­ be the family of all σ¡algebras containing E: Note ­ 6= ; as P(X) 2 ­: De¯ne M(E) = \M2­M: We want to show M(E) is a σ¡algebra. 1. ;; X 2 M(E) since ;; X 2 M for all M 2 ­: 2. Let E 2 M(E): Then E 2 M for all M 2 ­ which implies EC 2 M for all M 2 ­ which implies EC 2 M(E): 1 1 1 3. If fEjgj=1 ½ M(E); then fEjgj=1 ½ M for all M 2 ­ which implies [1 Ej 2 M for all M 2 ­ which implies 1 [1 Ej 2 M(E): Remark. M(E) is called the σ¡algebra generated by E: De¯nition. Let (X; T ) be a topological space. The σ¡algebra generated by T is called the Borel σ¡algebra on X and is denoted BX: The members of a Borel σ¡algebra are called Borel sets. Proposition 1 (p 22). BR is generated by each of the following: 1. E1 = f(a; b): a < bg 2. E2 = f[a; b]: a < bg 3. E3 = f(a; b]: a < bg or E4 = f[a; b): a < bg 4. E5 = f(a; 1): a 2 Rg or E6 = f(¡1; a): a 2 Rg 5. E7 = f[a; 1): a 2 Rg or E8 = f(¡1; a]: a 2 Rg Proof. In text. Remark. The Borel σ¡algebra on R is BR = fE ⊆ R : E \ R 2 BRg: It can be generated by E = f(a; 1]: a 2 Rg: Proposition 2. If (X; M) is a measurable space and f :X ! R; then TFAE 1. f is measurable 2. f ¡1((a; 1)) 2 M for all a 2 R 3. f ¡1([a; 1)) 2 M for all a 2 R 4. f ¡1((¡1; a)) 2 M for all a 2 R 5. f ¡1((¡1; a]) 2 M for all a 2 R Proposition 3 (p 43). Let (X; M) and (Y; N ) be measurable spaces. If N is generated by E ⊆ P(Y); then f :X ! Y is (M; N )¡measurable if and only if f ¡1(E) 2 M for all E 2 E: Proof. ()) Since E ⊆ N ; if f is measurable then f ¡1(E) 2 M for all E 2 E by de¯nition. (() De¯ne O = fE 2 Y: f ¡1(E) 2 Mg: Want to show O is a σ¡algebra. Then since E ⊆ O and N is generated by E; ¡1 C ¡1 C ¡1 1 1 ¡1 we will be able to conclude N ⊆ O: Recall (p4 of text) f (E ) = [f (E)] and f ([1 Ej) = [1 f (Ej): Claim: O is a σ¡algebra. Proof: 1. Since f ¡1(;) = ; and f ¡1(Y) = f ¡1(;C ) = (f ¡1(;))C = (;)C = X 2 M; we have ;; Y 2 O: 2. Suppose F 2 O: Then f ¡1(F ) 2 M by de¯nition of O and f ¡1(F C ) = [f ¡1(F )]C 2 M since M is a σ¡algebra. 1 ¡1 1 ¡1 1 1 ¡1 3. Suppose fFjgj=1 2 O: Then ff (Fj)gj=1 2 M and f ([j=1Fj) = [j=1f (Fj) 2 M as M is a σ¡algebra. 1 Thus [j=1Fj 2 O: Hence O is a σ¡algebra on Y; which contains E: Then N 2 O implies f is measurable.
Load more

Recommended publications
  • On Absolute Continuity∗
    journal of mathematical analysis and applications 222, 64–78 (1998) article no. AY975804 On Absolute Continuity∗ Zolt´an Buczolich† Department of Analysis, Eotv¨ os¨ Lorand´ University, Muzeum´ krt. 6–8, H–1088, Budapest, Hungary View metadata, citation and similar papers at core.ac.ukand brought to you by CORE provided by Elsevier - Publisher Connector Washek F. Pfeffer ‡ Department of Mathematics, University of California, Davis, California 95616 Submitted by Brian S. Thomson Received June 5, 1997 We prove that in any dimension a variational measure associated with an additive continuous function is σ-finite whenever it is absolutely continu- ous. The one-dimensional version of our result was obtained in [1] by a dif- ferent technique. As an application, we establish a simple and transparent relationship between the Lebesgue integral and the generalized Riemann integral defined in [7, Chap. 12]. In the process, we obtain a result (The- orem 4.1) involving Hausdorff measures and Baire category, which is of independent interest. As variations defined by BV sets coincide with those defined by figures [8], we restrict our attention to figures. The set of all real numbers is denoted by , and the ambient space of this paper is m where m 1 is a fixed integer. In m we use exclusively the metric induced by the maximum≥ norm . The usual inner product of · x; y m is denoted by x y, and 0 denotes the zero vector of m. For an ∈ · x m and ε>0, we let ∈ B x y m x y <ε : ε = ∈ x − *The results of this paper were presented to the Royal Belgian Academy on June 3, 1997.
    [Show full text]
  • Absolute Continuity for Operator Valued Completely Positive Maps on C
    CORE Metadata, citation and similar papers at core.ac.uk Provided by Bilkent University Institutional Repository JOURNAL OF MATHEMATICAL PHYSICS 50, 022102 ͑2009͒ Absolute continuity for operator valued completely algebras-ءpositive maps on C ͒ ͒ Aurelian Gheondea1,a and Ali Şamil Kavruk2,b 1Department of Mathematics, Bilkent University, Bilkent, Ankara 06800, Turkey and Institutul de Matematică al Academiei Române, C. P. 1-764, 014700 Bucureşti, Romania 2Department of Mathematics, University of Houston, Houston, Texas 77204-3476, USA ͑Received 15 October 2008; accepted 16 December 2008; published online 11 February 2009͒ Motivated by applicability to quantum operations, quantum information, and quan- tum probability, we investigate the notion of absolute continuity for operator valued algebras, previously introduced by Parthasarathy-ءcompletely positive maps on C ͓in Athens Conference on Applied Probability and Time Series Analysis I ͑Springer- Verlag, Berlin, 1996͒, pp. 34–54͔. We obtain an intrinsic definition of absolute continuity, we show that the Lebesgue decomposition defined by Parthasarathy is the maximal one among all other Lebesgue-type decompositions and that this maxi- mal Lebesgue decomposition does not depend on the jointly dominating completely positive map, we obtain more flexible formulas for calculating the maximal Le- besgue decomposition, and we point out the nonuniqueness of the Lebesgue de- composition as well as a sufficient condition for uniqueness. In addition, we con- sider Radon–Nikodym derivatives for absolutely continuous completely positive maps that, in general, are unbounded positive self-adjoint operators affiliated to a certain von Neumann algebra, and we obtain a spectral approximation by bounded Radon–Nikodym derivatives. An application to the existence of the infimum of two completely positive maps is indicated, and formulas in terms of Choi’s matrices for the Lebesgue decomposition of completely positive maps in matrix algebras are obtained.
    [Show full text]
  • The Absolutely Continuous Spectrum of the Almost Mathieu Operator
    THE ABSOLUTELY CONTINUOUS SPECTRUM OF THE ALMOST MATHIEU OPERATOR ARTUR AVILA Abstract. We prove that the spectrum of the almost Mathieu operator is absolutely continuous if and only if the coupling is subcritical. This settles Problem 6 of Barry Simon’s list of Schr¨odinger operator problems for the twenty-first century. 1. Introduction This work is concerned with the almost Mathieu operator H = Hλ,α,θ defined on ℓ2(Z) (1.1) (Hu)n = un+1 + un−1 +2λ cos(2π[θ + nα])un where λ = 0 is the coupling, α R Q is the frequency and θ R is the phase. This is the6 most studied quasiperiodic∈ \ Schr¨odinger operator, arisin∈ g naturally as a physical model (see [L3] for a recent historical account and for the physics back- ground). We are interested in the decomposition of the spectral measures in atomic (corre- sponding to point spectrum), singular continuous and absolutely continuous parts. Our main result is the following. Main Theorem. The spectral measures of the almost Mathieu operator are abso- lutely continuous if and only if λ < 1. | | 1.1. Background. Singularity of the spectral measures for λ 1 had been pre- viously established (it follows from [LS], [L1], [AK]). Thus| | the ≥ Main Theorem reduces to showing absolute continuity of the spectral measures for λ < 1, which is Problem 6 of Barry Simon’s list [S3]. | | We recall the history of this problem (following [J]). Aubry-Andr´econjectured the following dependence on λ of the nature of the spectral measures: arXiv:0810.2965v1 [math.DS] 16 Oct 2008 (1) (Supercritical regime) For λ > 1, spectral measures are pure point, (2) (Subcritical regime) For λ| <| 1, spectral measures are absolutely continu- ous.
    [Show full text]
  • Absolute Continuity of Poisson Random Fields
    Publ. RIMS, Kyoto Univ. 26 (1990), 629-647 Absolute Continuity of Poisson Random Fields By Yoichiro TAKAHASHI* § 0. Introduction Let R be a locally compact Hausdorff space with countable basis. Given a nonatomic nonnegative Radon measure 2. on R, we denote the Poisson measure (or random fields or point processes) with intensity 1 by TT^. Theorem. Let X and p be two nonatomic infinite nonnegative Radon measures on R. Then the Poisson measures KZ and TCP are mutually absolutely continuous if and only if (a) 2 and p are mutually absolutely continuous and (b) the Hellinger distance d(p, 1} between 1 and p is finite: (1) d(p Furthermore, the Hellinger distance D(xp, m) between KP and KI is then given by the formula /o\ D(rr T \^ ' The main purpose of the present note is to give a proof to Theorem above. In the last section we shall apply Theorem to the problem of giving the precise definition of the Fisher information or, equivalently, of giving a statistically natural Riemannian metric for an "infinite dimensional statistical model", which consists of mutually absolutely continuous Poisson measures. Remark, (i) Assume (a) and denote d (3) A- P Y~ dl ' Then the Radon-Nikodym derivative ^ is positive and finite ^-almost everywhere Communicated by H. Araki, October 23, 1989. * Department of Pure and Applied Sciences, College of Arts and Sciences, University of Tokyo, Komaba, Tokyo 153, Japan. 630 YOICHIRO TAKAHASHI and the condition (b) means that (10 V0~-1<EEL\R, X). In fact, by definition, (ii) The case where R is an Euclidean space is already studied by A.V.
    [Show full text]
  • On the Existence of Monotone Pure Strategy Equilibria in Bayesian Games
    On the Existence of Monotone Pure Strategy Equilibria in Bayesian Games Philip J. Reny Department of Economics University of Chicago January 2006 Abstract We extend and strengthen both Athey’s (2001) and McAdams’(2003) results on the existence of monotone pure strategy equilibria in Bayesian games. We allow action spaces to be compact locally-complete metrizable semilatttices and can handle both a weaker form of quasisupermodularity than is employed by McAdams and a weaker single-crossing property than is required by both Athey and McAdams. Our proof — which is based upon contractibility rather than convexity of best reply sets — demonstrates that the only role of single-crossing is to help ensure the existence of monotone best replies. Finally, we do not require the Milgrom-Weber (1985) absolute continuity condition on the joint distribution of types. 1. Introduction In an important paper, Athey (2001) demonstrates that a monotone pure strategy equilib- rium exists whenever a Bayesian game satis…es a Spence-Mirlees single-crossing property. Athey’s result is now a central tool for establishing the existence of monotone pure strat- egy equilibria in auction theory (see e.g., Athey (2001), Reny and Zamir (2004)). Recently, McAdams (2003) has shown that Athey’sresults, which exploit the assumed total ordering of the players’one-dimensional type and action spaces, can be extended to settings in which type and action spaces are multi-dimensional and only partially ordered. This permits new existence results in auctions with multi-dimensional signals and multi-unit demands (see McAdams (2004)). At the heart of the results of both Athey (2001) and McAdams (2003) is a single-crossing assumption.
    [Show full text]
  • Topics in Analysis 1 — Real Functions
    Topics in analysis 1 | Real functions Onno van Gaans Version May 21, 2008 Contents 1 Monotone functions 2 1.1 Continuity . 2 1.2 Differentiability . 2 1.3 Bounded variation . 2 2 Fundamental theorem of calculus 2 2.1 Indefinite integrals . 2 2.2 The Cantor function . 2 2.3 Absolute continuity . 2 3 Sobolev spaces 2 3.1 Sobolev spaces on intervals . 3 3.2 Application to differential equations . 6 3.3 Distributions . 10 3.4 Fourier transform and fractional derivatives . 16 4 Stieltjes integral 20 4.1 Definition and properties . 20 4.2 Langevin's equation . 28 4.3 Wiener integral . 32 5 Convex functions 33 1 1 Monotone functions 1.1 Continuity 1.2 Differentiability 1.3 Bounded variation 2 Fundamental theorem of calculus 2.1 Indefinite integrals 2.2 The Cantor function 2.3 Absolute continuity 3 Sobolev spaces When studying differential equations it is often convenient to assume that all functions in the equation are several times differentiable and that their derivatives are continuous. If the equation is of second order, for instance, one usually assumes that the solution should be two times continuously differentiable. There are several reasons to allow functions with less smoothness. Let us consider two of them. Suppose that u00(t) + αu0(t) + βu(t) = f(t) describes the position u(t) of a particle at time t, where f is some external force acting on the particle. If the force is contiuous in t, one expects the solution u to be twice continuously differentiable. If the functions f has jumps, the second derivative of u will not be continuous and, more than that, u0 will probably not be differentiable at the points where f is discon- tinuous.
    [Show full text]
  • The Converse Envelope Theorem∗
    The converse envelope theorem∗ Ludvig Sinander Northwestern University 14 July 2021 Abstract I prove an envelope theorem with a converse: the envelope formula is equivalent to a first-order condition. Like Milgrom and Segal’s (2002) envelope theorem, my result requires no structure on the choice set. I use the converse envelope theorem to extend to abstract outcomes the canonical result in mechanism design that any increasing allocation is implementable, and apply this to selling information. 1 Introduction Envelope theorems are a key tool of economic theory, with important roles in consumer theory, mechanism design and dynamic optimisation. In blueprint form, an envelope theorem gives conditions under which optimal decision- making implies that the envelope formula holds. In textbook accounts,1 the envelope theorem is typically presented as a consequence of the first-order condition. The modern envelope theorem of Milgrom and Segal (2002), however, applies in an abstract setting in which the first-order condition is typically not even well-defined. These authors therefore rejected the traditional intuition and developed a new one. In this paper, I re-establish the intuitive link between the envelope formula arXiv:1909.11219v4 [econ.TH] 14 Jul 2021 and the first-order condition. I introduce an appropriate generalised first-order ∗I am grateful to Eddie Dekel, Alessandro Pavan and Bruno Strulovici for their guidance and support. This work has profited from the close reading and insightful comments of Gregorio Curello, Eddie Dekel, Roberto Saitto, Quitzé Valenzuela-Stookey, Alessandro Lizzeri and four anonymous referees, and from comments by Piotr Dworczak, Matteo Escudé, Benny Moldovanu, Ilya Segal and audiences at Northwestern and the 2021 Kansas Workshop in Economic Theory.
    [Show full text]
  • Absolute Continuity of the Integrated Density of States for the Almost Mathieu Operator with Non-Critical Coupling
    ABSOLUTE CONTINUITY OF THE INTEGRATED DENSITY OF STATES FOR THE ALMOST MATHIEU OPERATOR WITH NON-CRITICAL COUPLING ARTUR AVILA AND DAVID DAMANIK Abstract. We show that the integrated density of states of the almost Math- ieu operator is absolutely continuous if and only if the coupling is non-critical. We deduce for subcritical coupling that the spectrum is purely absolutely continuous for almost every phase, settling the measure-theoretical case of Problem 6 of Barry Simon’s list of Schr¨odinger operator problems for the twenty-first century. 1. Introduction This work is concerned with the almost Mathieu operator H = Hλ,α,θ defined on `2(Z) (1) (Hu)n = un+1 + un−1 + 2λ cos(2π[θ + nα])un where λ 6= 0 is the coupling, α ∈ R\Q is the frequency, and θ ∈ R is the phase. This is the most heavily studied quasiperiodic Schr¨odinger operator, arising naturally as a physical model (see [22] for a recent historical account and for the physics background). We are interested in the integrated density of states, which can be defined as the limiting distribution of eigenvalues of the restriction of H = Hλ,α,θ to large finite intervals. This limiting distribution (which exists by [7]) turns out to be a θ independent continuous increasing surjective function N = Nλ,α : R → [0, 1]. The support of the probability measure dN is precisely the spectrum Σ = Σλ,α. Another important quantity, the Lyapunov exponent L = Lλ,α, is connected to the integrated density of states by the Thouless formula L(E) = R ln |E − E0| dN(E0).
    [Show full text]
  • FUNCTIONS of BOUNDED VARIATION 1. Introduction in This Paper We Discuss Functions of Bounded Variation and Three Related Topics
    FUNCTIONS OF BOUNDED VARIATION NOELLA GRADY Abstract. In this paper we explore functions of bounded variation. We discuss properties of functions of bounded variation and consider three re- lated topics. The related topics are absolute continuity, arc length, and the Riemann-Stieltjes integral. 1. Introduction In this paper we discuss functions of bounded variation and three related topics. We begin by defining the variation of a function and what it means for a function to be of bounded variation. We then develop some properties of functions of bounded variation. We consider algebraic properties as well as more abstract properties such as realizing that every function of bounded variation can be written as the difference of two increasing functions. After we have discussed some of the properties of functions of bounded variation, we consider three related topics. We begin with absolute continuity. We show that all absolutely continuous functions are of bounded variation, however, not all continuous functions of bounded variation are absolutely continuous. The Cantor Ternary function provides a counter example. The second related topic we consider is arc length. Here we show that a curve has a finite length if and only if it is of bounded variation. The third related topic that we examine is Riemann-Stieltjes integration. We examine the definition of the Riemann-Stieltjes integral and see when functions of bounded variation are Riemann-Stieltjes integrable. 2. Functions of Bounded Variation Before we can define functions of bounded variation, we must lay some ground work. We begin with a discussion of upper bounds and then define partition.
    [Show full text]
  • Proofs of Theorems and Corollaries for “Quantile Regression Under Misspecification, with an Application to the U.S
    Proofs of Theorems and Corollaries for “Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure” Joshua Angrist, Victor Chernozhukov, and Iv´anFern´andez-Val This supplement to the paper “Quantile Regression under Misspecification, with an Application to the U.S. Wage Structure” provides added technical details related to the proofs of the theorems and corollaries not included in the main text. In particular, it contains the proofs for Theorem 3 on the uniform consistency and asymptotic Gaussianity of the sample QR process and the proofs for the Corollaries of this Theorem, along with the proofs of uniform consistency for the estimators of the components of the covariance kernel. 1 Proof of Theorems 3 and its Corollaries The proof has two steps.1 The first step establishes uniform consistency of the sample QR process. The second step establishes asymptotic Gaussianity of the sample QR process.2 For W = (Y, X), let −1 Pn −1/2 Pn En [f(W )] denote n i=1 f(Wi) and Gn [f(W )] denote n i=1(f(Wi) − E [f(Wi)]). If fb is an estimated function, [f(W )] denotes n−1/2 Pn (f(W ) − E [f(W )]) . For a matrix A, mineig [A] Gn b i=1 i i f=fb denotes the minimum eigenvalue of A. 1.1 Uniform consistency of βˆ(·) ˆ 0 0 For each τ in T , β(τ) minimizes Qn(τ, β) := En [ρτ (Y − X β) − ρτ (Y − X β(τ))] . Define Q∞(τ, β) := 0 0 0 E [ρτ (Y − X β) − ρτ (Y − X β(τ))] .
    [Show full text]
  • Absolute Continuity and Uniqueness of Measures on Metric Spaces by 1 Pertti Mattila
    transactions of the american mathematical society Volume 266, Number 1, July 1981 ABSOLUTE CONTINUITY AND UNIQUENESS OF MEASURES ON METRIC SPACES BY 1 PERTTI MATTILA Abstract. We show that if two Borel regular measures on a separable metric space are in a suitable sense homogeneous, then they are mutually absolutely continuous. We use such absolute continuity theorems together with some density theorems to prove the uniqueness of measures more general than Haar measures. 1. Introduction. In this paper we prove absolute continuity and uniqueness theorems on a separable metric space X for Borel regular measures which are homogeneous in the sense that the measures of the small balls of the same radii are roughly of the same magnitude. We shall consider two cases separately. In the first case we consider measures ¡i which satisfy u.B(x, 5r) hm sup -^. —r¿ < o° for x E X. (1) ri0 F ixB(x, r) S ' (The number 5 comes from covering theorems, but obviously (1) holds if and only if it holds when 5 is replaced with some t > 1.) In the second case we assume no 5 r-condition, but then we must make stronger homogeneity assumptions. (For the necessity of these see Remark 4.8.) We call the first case finite dimensional and the second infinite dimensional because (1) holds for several interesting measures on finite-dimensional spaces, whereas it is false in typically infinite-dimensional cases. Let u, and ¡i2 he Borel regular measures on X. In the absolute continuity theorems we prove that if there are nondecreasing functions n,: (0, oo)—»(0, co) with limri0 h¡(r) = 0, i = 1,2, such that either n.u .
    [Show full text]
  • Functions of Bounded Variation and Absolutely Continuous Functions
    209: Honors Analysis in Rn Functions of bounded variation and absolutely continuous functions 1 Nondecreasing functions Definition 1 A function f :[a, b] → R is nondecreasing if x ≤ y ⇒ f(x) ≤ f(y) holds for all x, y ∈ [a, b]. Proposition 1 If f is nondecreasing in [a, b] and x0 ∈ (a, b) then lim f(x) and lim f(x) x→x0, x<x0 x→x0, x>x0 exist. We denote them f(x0 − 0) and, respectively f(x0 + 0). Proof Exercise. Definition 2 The function f is sait to be continuous from the right at x0 ∈ (a, b) if f(x0 + 0) = f(x0). It is said to be continuous from the left if f(x0 − 0) = f(x0). A function is said to have a jump discontinuity at x0 if the limits f(x0 ± 0) exist and if |f(x0 + 0) − f(x0 − 0)| > 0. Let x1, x2 ... be a sequence in [a, b] and let hj > 0 be a sequence of positive P∞ numbers such that n=1 hn < ∞. The nondecreasing function X f(x) = hn xn<x is said to be the jump function associated to the sequences {xn}, {hn}. Proposition 2 Let f be nondecreasing on [a, b]. Then it is measurable and bounded and hence it is Lebesgue integrable. Proof Exercise. (Hint: the preimage of (−∞, α) is an interval.) 1 Proposition 3 Let f :[a, b] → R be nondecreasing. Then all the discon- tinuities of f are jump discontinuities. There are at most countably many such. Proof Exercise. (Hint: The range of the function is bounded, and the total length of the range is larger than the sum of the absolute values of the jumps, so for each fixed length, there are at most finitely many jumps larger than that length.) Exercise 1 A jump function associated to {xn}{hn} is continuous from the left and jumps hn = f(xn + 0) − f(xn − 0).
    [Show full text]