DOCSLIB.ORG

THE CODIFFERENCE FUNCTION for A-STABLE PROCESSES: ESTIMATION and INFERENCE

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
THE CODIFFERENCE FUNCTION for A-STABLE PROCESSES: ESTIMATION and INFERENCE Die approbierte Originalversion dieser Dissertation ist an der Hauptbibliothek der Technischen Universität Wien aufgestellt (http://www.ub.tuwien.ac.at). The approved original version of this thesis is available at the main library of the Vienna University of Technology (http://www.ub.tuwien.ac.at/englweb/). DISSERTATION THE CODIFFERENCE FUNCTION FOR a-STABLE PROCESSES: ESTIMATION AND INFERENCE ausgeführt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Wissenschaften unter der Leitung von o. UNIV. PROF. DIPL.-ING. DR. TECHN. MANFRED DEISTLER E 105 Institut für Wirtschaftsmathematik eingereicht an der Technischen Universität Wien Fakultät für Mathematik und Geoinformation von DEDI ROSADI Matrikelnummer: 0126963 Lorenz Müllergasse 1A/5209, A1200 Wien Wien, am 27.08.2004 This thesis is dedicated to My beloved Dad died December 13, 2001 May God rest his soul in peace. Contents Abstract v Kurzfassung vii Acknowledgement ix 1 Introduction 1 1.1 Motivation and background 1 1.2 Some problems in heavy-tail modelling 5 1.3 Thesis contribution 7 2 The a-stable distribution family 9 2.1 A short historical overview 9 2.2 Basic facts 10 2.2.1 Univariate stable distribution 10 2.2.2 Multivariate stable distribution 13 2.2.3 Stable stochastic processes 15 2.3 Fitting data to the stable distributions 16 2.4 The estimation of the stable distribution parameters 18 2.5 Generating the stable random variâtes 21 3 Review of heavy-tailed ARMA modelling 23 3.1 Heavy-tailed time series 23 3.2 Order selection of ARMA models 25 3.2.1 Estimation of the autocorrelation function 25 3.2.2 Estimation of the autocovariation function 27 3.2.3 Notes on the graphical order identification procedures 29 3.3 Parameter estimation of ARMA models 29 3.3.1 Yule-Walker and Generalized Yule-Walker Estimator 30 3.3.2 M-estimator 30 3.3.3 Summary of the other results 33 3.4 Diagnostic checking 33 iii iv CONTENTS 4 The codifference function 35 4.1 Definition of the codifference function 35 4.2 The asymptotic behavior of the codifference function 38 4.3 The estimator of the codifference function 46 4.4 The asymptotic properties of the estimator 47 4.5 Proof of Theorem 4.4.1 48 4.6 Proof of Theorem 4.4.2 50 4.7 Proof of Corollary 4.4.3 57 5 Application 61 5.1 Practical considerations 61 5.1.1 The choice of s 61 5.1.2 Simulation results 63 5.2 Order identification and estimation of a-stable moving averages 71 5.2.1 Introduction 71 5.2.2 Fitting moving average process 71 5.2.3 Simulation results 72 5.3 Testing for independence in heavy-tailed time series 79 5.3.1 Introduction 79 5.3.2 A Portmanteau-type test using the codifference function 80 5.3.3 Simulation results 81 6 Conclusions 87 A Appendix 89 A.I Some concepts related to the stable law 89 A.2 Stochastic processes 91 A.3 The autocovariance function 94 A.4 The central limit theorem for ECF of linear processes 95 A.5 The complex logarithmic function 95 Abstract In this thesis, we consider a strictly stationary univariate symmetric a-stable process {Xt, t € Z} with 0 < a < 2. For Gaussian process (a = 2), the covariance function completely describes the dependence structure over time for {Xt}. For a < 2, the second moments of the process are infinite, and therefore the population covariance function is not defined. However, the (noncentral) sample autocovaxiance and the sample autocorrelation function (SACF) are well defined random variables. Davis and Resnick (1986) showed that for a class of linear models with infinite variance, the SACF will converge, and when it is properly normalized, converges weakly to a limiting distribution. Unfortunately, as a tool for modelling, the SACF of linear time series models with infinite variance has some drawbacks (see Section 1.2). Due to the drawbacks of the SACF, for processes having no population second moments, it is desir- able to have a dependence measure which does not depend on the existence of (second) moments. For this purpose, some generalizations of the autocovariance function as dependence measures of stationary process with infinite variance have been proposed in literature, e.g., the autocovaria- tion (Cambanis and Miller, 1981), the codifference function (Kokoszka and Taqqu, 1994) and the dynamical function (Janicki and Weron, 1994&). In this thesis we study the codifference function and also consider the normalized codifference function of causal stable ARMA process with sym- metric a-stable noise, 0 < a < 2. We consider estimators of the codifference and the normalized codifference function based on the empirical characteristic function. We further show the asymp- totic properties of the proposed estimators. Finally, we discuss the application of the codifference function for identifying and estimating moving average models and doing Portmanteau-type test. This thesis is organized as follows. In the first part of this thesis, comprising Chapters 1-3, we present some important concepts, which will be required for further understanding of the thesis. In the introductory chapter, the background and motivations to the problem that we consider are presented. Chapter 2 gives the reader an overview of stable distributions. Chapter 3 provides an overview of results related to the a-stable ARMA modelling. The second part, consisting of Chapters 4-6, deals with the main results. Chapter 4 treats the codifference function. Here we provide the definition of the function, and give a summary of the asymptotic properties of the codifference function for causal stable ARMA case. Furthermore, based on the empirical characteristic function, we propose estimators of the codifference and the normalized codifference function. We show the consistency of the proposed estimators, where the underlying model is causal stable ARMA with symmetric a-stable noise, 0 < a < 2. In addition, we establish their limiting distributions. Chapter 5 deals with the application of the results presented in Chapter 4. In the first section of this chapter, we address some practical issues for the calculation of the sample codifference and the normalized codifference function from time series data with finite size. In section two, we describe an order identification and estimation method for moving average models which uses the codifference function. In the last section, we discuss the application of the codifference function for Portmanteau-type test, i.e., testing for independence against serial dependence alternatives. In the last chapter, we provide a brief summary, draw conclusions from the results presented in this thesis and outline possible further research directions. Kurzfassung In dieser Dissertation betrachten wir einen strikt stationären univariaten symmetrische a-stabilen Prozeß {Xt,t 6 Z}, wobei 0 < a < 2. Für einen Gaußschen Prozeß (a = 2) beschreibt die Kovarianzfunktion in eineindeutiger Weise die Abhängigkeitsstruktur von {Xt} über die Zeit hin- weg. Für a < 2 existieren die zweiten Momente des Prozesses hingegen nicht, und daher ist auch die Kovarianzfunktion nicht definiert. Die (nichtzentrierte) Sample-Autokovarianzfunktion und die Sample-Autokorrelationsfunktion (ACF) sind hingegen wohldefinierte Zufalls variablen. Davis and Resnick (1986) haben gezeigt, daß für eine Klasse von linearen Modellen mit unbeschränkter Varianz die Sample-ACF konvergiert, und daß, bei geeigneter Normalisierung, schwache Konver- genz gegen eine Grenzverteilung vorliegt. Leider hat die Sample-ACF als Modellierung-Tool bei linearen Zeitreihenmodellen mit unbeschränkter Varianz einige Nachteile (siehe Abschnitt 1.2). Wegen der oben angesprochenen Nachteile der Sample-ACF bei Prozessen mit unbeschränkten zweiten Momenten ist es wünschenswert ein Abhängigkeitsmaß zu betrachten, welches nicht von der Existenz (zweiter) Momente abhängt. Zu diesem Zweck wurden in der Literatur einige Ver- allgemeinerungen der Autokovarianzfunktion als Abhängigkeitsmaß für stationäre Prozesse mit unbeschränkter Varianz vorgeschlagen. Beispiele hierfür sind die Autokovariation (Cambanis and Miller, 1981), die Kodifferenzfunktion (Kokoszka and Taqqu, 1994) und die dynamische Funktion (Janicki and Weron, 19946). In dieser Dissertation betrachten wir die Kodifferenzfunktion und die normalisierte Kodifferenzfunktion eines kausalen stabilen ARMA Prozeß mit symmetrischem a-stabilen Rauschen, 0 < a < 2. Die Schätzer, welche wir für diese Funktionen definieren, basieren auf der empirischen charakteristischen Funktion. Wir zeigen die asymptotischen Eigenschaften der Schätzer und diskutieren die Anwendung der Kodifferenzfunktion bei statistischen Tests, die dem Portmanteau Test sehr ähnlich sind und bei der Identifizierung und Schätzung von MA-Modellen. Die Dissertation ist wie folgt gegliedert: Im ersten Teil, welcher aus den Kapiteln 1-3 besteht, präsentieren wir wichtige Konzepte, die für das Verständnis der Arbeit vonnöten sind. In der Einleitung wird der Hintergrund und die Motivation des betrachteten Problems beleuchtet. In Kapitel 2 erhält der Leser eine Übersicht über stabile Verteilungen. Kapitel 3 bietet eine Übersicht über bestehende Resultate zur a-stabilen ARMA-Modellierung. Der zweite Teil der Arbeit besteht aus den Kapiteln 4-6 und beinhaltet die Hauptresultate. Kapitel 4 beschäftigt sich mit der Kodifferenzfunktion. Wir geben eine Definition der besagten Funktion und eine Zusammenfassung der asymptotischen Eigenschaften der Kodifferenzfunktion im kausalen stabilen ARMA Prozeß. Weiters schlagen wir Schätzer der Kodifferenz- und norma- lisierten Kodifferenzfunktion vor, die auf der empirischen charakteristischen
Load more

Recommended publications
  • The Holtsmark-Continuum Model for the Statistical Description of a Plasma
    On fluctuations in plasmas : the Holtsmark-continuum model for the statistical description of a plasma Citation for published version (APA): Dalenoort, G. J. (1970). On fluctuations in plasmas : the Holtsmark-continuum model for the statistical description of a plasma. Technische Hogeschool Eindhoven. https://doi.org/10.6100/IR108607 DOI: 10.6100/IR108607 Document status and date: Published: 01/01/1970 Document Version: Publisher’s PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication: • A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.
    [Show full text]
  • On Levy-Walk Model for Correlations in Spatial Galaxy Distribution
    Physics & Astronomy International Journal Mini Review Open Access On levy-walk model for correlations in spatial galaxy distribution Abstract Volume 3 Issue 2 - 2019 The model of stochastic fractal is briefly described, basic results regarding properties VV Uchaikin of spatial structures with long-range correlations of power-law type are reviewed, and Ulyanovsk State University, 42 Lev Tolstoy Str., Ulyanovsk some applications to galaxy distribution in the Universe are discussed. 432700, Russia Correspondence: VV Uchaikin, Ulyanovsk State University, 42 Lev Tolstoy Str., Ulyanovsk 432700, Russia, Email Received: March 14, 2019 | Published: March 29, 2019 Introduction distribution of gravitational force magnitude (Holtsmark distribution). Section IV presents the distribution of number of points inside the One of important problems complicating the extraction of spherical cells in the stochastic fractal model. In Section V the question information from cosmological surveys, is necessity to supplement about the global mass density of the fractal Universe is investigated observational data subtracted of foreground or for some (i.e. with the empirical distribution density over masses adopted. In Section technical) reasons removed from the survey. This particularly VI the ergodic problem in observational cosmology is discussed. affects the extraction of information from the largest observable scales. Maximum-likelihood estimators are not quite applicable for Power spectrum reconstructing the full-sky because of non-Caussian character of The important statistical characteristic of the large-scale structure observed matter distribution. To solve this problem, many authors of the Universe is the two-point spatial galaxy correlation function,1 modify implementations of such estimators which are robust to the which determines the joint probability leakage of contaminants from within masked regions.
    [Show full text]
  • Field Guide to Continuous Probability Distributions
    Field Guide to Continuous Probability Distributions Gavin E. Crooks v 1.0.0 2019 G. E. Crooks – Field Guide to Probability Distributions v 1.0.0 Copyright © 2010-2019 Gavin E. Crooks ISBN: 978-1-7339381-0-5 http://threeplusone.com/fieldguide Berkeley Institute for Theoretical Sciences (BITS) typeset on 2019-04-10 with XeTeX version 0.99999 fonts: Trump Mediaeval (text), Euler (math) 271828182845904 2 G. E. Crooks – Field Guide to Probability Distributions Preface: The search for GUD A common problem is that of describing the probability distribution of a single, continuous variable. A few distributions, such as the normal and exponential, were discovered in the 1800’s or earlier. But about a century ago the great statistician, Karl Pearson, realized that the known probabil- ity distributions were not sufficient to handle all of the phenomena then under investigation, and set out to create new distributions with useful properties. During the 20th century this process continued with abandon and a vast menagerie of distinct mathematical forms were discovered and invented, investigated, analyzed, rediscovered and renamed, all for the purpose of de- scribing the probability of some interesting variable. There are hundreds of named distributions and synonyms in current usage. The apparent diver- sity is unending and disorienting. Fortunately, the situation is less confused than it might at first appear. Most common, continuous, univariate, unimodal distributions can be orga- nized into a small number of distinct families, which are all special cases of a single Grand Unified Distribution. This compendium details these hun- dred or so simple distributions, their properties and their interrelations.
    [Show full text]
  • Characteristic Kernels and Infinitely Divisible Distributions
    Journal of Machine Learning Research 17 (2016) 1-28 Submitted 3/14; Revised 5/16; Published 9/16 Characteristic Kernels and Infinitely Divisible Distributions Yu Nishiyama [email protected] The University of Electro-Communications 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan Kenji Fukumizu [email protected] The Institute of Statistical Mathematics 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan Editor: Ingo Steinwart Abstract We connect shift-invariant characteristic kernels to infinitely divisible distributions on Rd. Characteristic kernels play an important role in machine learning applications with their kernel means to distinguish any two probability measures. The contribution of this paper is twofold. First, we show, using the L´evy–Khintchine formula, that any shift-invariant kernel given by a bounded, continuous, and symmetric probability density function (pdf) of an infinitely divisible distribution on Rd is characteristic. We mention some closure properties of such characteristic kernels under addition, pointwise product, and convolution. Second, in developing various kernel mean algorithms, it is fundamental to compute the following values: (i) kernel mean values mP (x), x , and (ii) kernel mean RKHS inner products ∈ X mP ,mQ H, for probability measures P,Q. If P,Q, and kernel k are Gaussians, then the computationh i of (i) and (ii) results in Gaussian pdfs that are tractable. We generalize this Gaussian combination to more general cases in the class of infinitely divisible distributions. We then introduce a conjugate kernel and a convolution trick, so that the above (i) and (ii) have the same pdf form, expecting tractable computation at least in some cases.
    [Show full text]
  • Lecture Notes on Nonequilibrium Statistical Physics (A Work in Progress)
    Lecture Notes on Nonequilibrium Statistical Physics (A Work in Progress) Daniel Arovas Department of Physics University of California, San Diego June 13, 2021 Contents 1 Fundamentals of Probability 1 1.1 References...................................... ........ 1 1.2 StatisticalPropertiesofRandomWalks . ............... 2 1.2.1 One-dimensionalrandomwalk . ........ 2 1.2.2 Thermodynamiclimit. ....... 4 1.2.3 Entropyandenergy.............................. ...... 5 1.3 BasicConceptsinProbabilityTheory . .............. 6 1.3.1 Fundamentaldefinitions . ....... 6 1.3.2 Bayesianstatistics . ......... 7 1.3.3 Randomvariablesandtheiraverages . ........... 8 1.4 EntropyandProbability . ........... 9 1.4.1 Entropyandinformationtheory. .......... 9 1.4.2 Probability distributions from maximum entropy . ............... 11 1.4.3 Continuousprobabilitydistributions . .............. 15 1.5 GeneralAspectsofProbabilityDistributions . .................. 16 1.5.1 Discreteandcontinuousdistributions . ............. 16 1.5.2 Centrallimittheorem . ........ 18 1.5.3 Momentsandcumulants .. ..... ...... ..... ...... .... ..... 19 1.5.4 MultidimensionalGaussianintegral . ........... 20 1.6 BayesianStatisticalInference . ............... 21 1.6.1 FrequentistsandBayesians. .......... 21 i ii CONTENTS 1.6.2 UpdatingBayesianpriors. ......... 22 1.6.3 Hyperparametersandconjugatepriors . ............ 23 1.6.4 Theproblemwithpriors . ....... 25 2 Stochastic Processes 27 2.1 References...................................... ........ 27 2.2 IntroductiontoStochasticProcesses . ...............
    [Show full text]
  • The “Lévy Or Diffusion” Controversy: How Important Is the Movement Pattern in the Context of Trapping?
    mathematics Article The “Lévy or Diffusion” Controversy: How Important Is the Movement Pattern in the Context of Trapping? Danish A. Ahmed 1,*, Sergei V. Petrovskii 2,* and Paulo F. C. Tilles 2,3 1 Department of Mathematics and Natural Sciences, Prince Mohammad Bin Fahd University, Al-Khobar, Dhahran 34754, Saudi Arabia 2 Department of Mathematics, University of Leicester, University Road, Leicester LE1 7RH, UK 3 Departament of Matematica, Universida de Federal de Santa Maria, Santa Maria CEP 97105-900, Brazil; [email protected] * Correspondence: [email protected] or [email protected] (D.A.A.); [email protected] (S.V.P.) Received: 30 March 2018; Accepted: 24 April 2018; Published: 9 May 2018 Abstract: Many empirical and theoretical studies indicate that Brownian motion and diffusion models as its mean field counterpart provide appropriate modeling techniques for individual insect movement. However, this traditional approach has been challenged, and conflicting evidence suggests that an alternative movement pattern such as Lévy walks can provide a better description. Lévy walks differ from Brownian motion since they allow for a higher frequency of large steps, resulting in a faster movement. Identification of the ‘correct’ movement model that would consistently provide the best fit for movement data is challenging and has become a highly controversial issue. In this paper, we show that this controversy may be superficial rather than real if the issue is considered in the context of trapping or, more generally, survival probabilities. In particular, we show that almost identical trap counts are reproduced for inherently different movement models (such as the Brownian motion and the Lévy walk) under certain conditions of equivalence.
    [Show full text]
  • Levy-Stable Distributions Revisited: Tail Index> 2 Does Not Exclude the Levy
    To be published in: International Journal of Modern Physics C (2001) vol. 12 no. 2 Levy-stable distributions revisited: tail index > 2 does not exclude the Levy-stable regime Rafa lWeron Hugo Steinhaus Center for Stochastic Methods, Wroc law University of Technology, 50-370 Wroc law, Poland E-mail: [email protected] Abstract: Power-law tail behavior and the summation scheme of Levy-stable distri- butions is the basis for their frequent use as models when fat tails above a Gaussian distribution are observed. However, recent studies suggest that financial asset re- turns exhibit tail exponents well above the Levy-stable regime (0 <α ≤ 2). In this paper we illustrate that widely used tail index estimates (log-log linear regression and Hill) can give exponents well above the asymptotic limit for α close to 2, re- sulting in overestimation of the tail exponent in finite samples. The reported value of the tail exponent α around 3 may very well indicate a Levy-stable distribution with α ≈ 1.8. Keywords: Levy-stable distribution, Tail exponent, Hill estimator, Econophysics 1 Introduction Levy-stable laws are a rich class of probability distributions that allow skewness arXiv:cond-mat/0103256v1 12 Mar 2001 and fat tails and have many intriguing mathematical properties [1]. They have been proposed as models for many types of physical and economic systems. There are several reasons for using Levy-stable laws to describe complex systems. First of all, in some cases there are solid theoretical reasons for expecting a non-Gaussian Levy- stable model, e.g. reflection off a rotating mirror yields a Cauchy distribution (α = 1), hitting times for a Brownian motion yield a Levy distribution (α =0.5, β = 1), the gravitational field of stars yields the Holtsmark distribution (α =1.5) [2, 3, 4].
    [Show full text]
  • Statistical Properties of the Gravitational Force in a Random Homogeneous Medium Constantin Payerne
    Statistical properties of the gravitational force in a random homogeneous medium Constantin Payerne To cite this version: Constantin Payerne. Statistical properties of the gravitational force in a random homogeneous medium. [Research Report] Université Grenoble-alpes; Laboratoire de Physique et de Modélisation des Milieux Condensés. 2020. hal-02551263 HAL Id: hal-02551263 https://hal.archives-ouvertes.fr/hal-02551263 Submitted on 22 Apr 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Statistical properties of the gravitational force in a random homogeneous medium Constantin PAYERNE, Universit´eGrenoble-Alpes, 2nd year of Master's degree in Subatomic Physics and Cosmology Supervisor : Vincent ROSSETTO, Laboratoire de Physique et de Mod´elisationdes Milieux Condens´es April 22, 2020 Abstract We discuss the statistical distribution of the gravitational (Newtonian) force exerted on a test particle in a infinite random and homogeneous gas of non-correlated particles (stars, galaxies, ...) where the first configuration of particles in space is a Poisson distribution. The exact solution is known as the Holtsmark distribution at the limit of infinite system corresponding to the number of particle N within the volume and the volume go to infinity.
    [Show full text]
  • Electric Microfield Distribution in Two-Component Plasmas. Theory
    Electric microfield distribution in two-component plasmas. Theory and Simulations a) b) a) J. Ortner ∗, I. Valuev , and W. Ebeling a)Institut f¨ur Physik, Humboldt Universit¨at zu Berlin, Invalidenstr. 110, D-10115 Berlin, Germany b)Department of Molecular and Chemical Physics, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia (to be published in Contr. Plasma Phys.) Abstract The distribution of the electric microfield at a charged particle moving in a two-component plasma is calculated. The theoretical approximations are obtained via the parameter integration technique and using the screened pair approximation for the generalized radial distribution function. It is shown that the two-component plasma microfield distribution shows a larger probability of high microfield values than the corresponding distribution of the commonly used OCP model. The theory is checked by quasiclassical molecular-dynamics simulations. For the simulations a corrected Kelbg pseudopotential has been used. arXiv:physics/9912019v1 [physics.plasm-ph] 9 Dec 1999 PACS: 52.25.Vy, 52.25.Gj, 52.65.-y, 05.30.-d Keywords: Two-component plasma; Electric microfield; Semiclassical molecular dynamics ∗Corresponding author, Tel.: (+4930) 2093 7636, email: [email protected] 1 I. INTRODUCTION The purpose of this paper is the investigation of the microfield distribution in a two- component plasma at the position of a charged particle. The determination of the distribution of the electric microfield component created by one of the subsystems separately - electron or ion - is a well studied problem (for a review see [1]). Holtsmark [2] reduced the line shape problem to the determination of the prob- ability distribution of perturbing ionic electric microfield.
    [Show full text]
  • NORM P ESTIMATION by Hans Nyquist AKADEMI
    Institute of Statistics University of Umeå S-901 87 Umeå Sweden RECENT STUDIES ON L -NORM P ESTIMATION by Hans Nyquist AKADEMISK AVHANDLING som med tillstånd av rektorsämbetet vid Umeå universitet för erhållande av filosofie doktorsexamen framlägges till offentlig granskning i Hörsal D, Samhällsvetarhuset, fredagen den 12 december 1980 kl 10.15. TABLE OF CONTENTS ABSTRACT ACKNOWLEDGEMENTS CHAPTER I. OVERVIEW OF THE PROBLEM 1 CHAPTER II. A BACKGROUND TO L -NORM ESTIMATION 5 P 2.1 An introduction to the art of combining observations 5 2.2 Linear models 15 2.3 Stable distributions and their domains of attraction 20 2.4 Examples of models with fat-tailed or contamined distributed random variables 26 2.5 Algorithms for computations of L^- and L^-norm estimates 35 2.6 Some Monte Carlo studies 39 2.7 Conclusions 52 CHAPTER III. SOME BASIC PROPERTIES OF L -NORM ESTIMATORS 55 P 3.1 Definition of the L -norm estimators 55 P 3.2 The existence of L -norm estimates 59 P 3.3 Characterizations of L -norm estimates 60 P 3.4 Uniqueness of L^-norm estimates, 1 < p < 00 66 3.5 Uniqueness of L^-norm estimates 67 3.6 Geometric interpretations 86 3.7 A general notion of orthogonality 89 3.8 Relations to M- and L-estimators 91 3.9 Equivalence of L^-norm and maximum likelihood estimators 93 3.10 Computation of L^-norm estimates, 1 < p < «>, p * 2 95 3.11 Conclusions 97 CHAPTER IV. SOME SAMPLING PROPERTIES OF L -NORM ESTIMATORS 100 P 4.1 Introduction 100 4.2 The sampling distribution of L^-norm estimators 102 4.3 Stochastic regressors 109 4.4 Heteroscedasticity 111 4.5 Linearly dependent residuals 114 4.6 The optimal L -norm estimator 123 P 4.7 Estimation of the variance 133 4.8 Geometrical interpretations 135 4.9 Conclusions 138 CHAPTER V.
    [Show full text]
  • Accurate and Efficient Numerical Calculation of Stable Densities Via
    Accurate and efficient numerical calculation of stable densities via optimized quadrature and asymptotics Sebastian Ament* and Michael O’Neil† August 31, 2021 Abstract Stable distributions are an important class of infinitely-divisible probability distributions, of which two special cases are the Cauchy distribution and the normal distribution. Aside from a few special cases, the density function for stable distributions has no known analytic form, and is expressible only through the variate’s characteristic function or other integral forms. In this paper we present numerical schemes for evaluating the density function for stable distributions, its gra- dient, and distribution function in various parameter regimes of interest, some of which had no pre-existing efficient method for their computation. The novel evaluation schemes consist of opti- mized generalized Gaussian quadrature rules for integral representations of the density function, complemented by asymptotic expansions near various values of the shape and argument parame- ters. We report several numerical examples illustrating the efficiency of our methods. The resulting code has been made available online. Keywords: Stable distributions, a-stable, generalized Gaussian quadrature, infinitely-divisible dis- tributions, numerical quadrature. 1 Introduction Continuous random variables that follow stable laws arise frequently in physics [6], finance and eco- nomics [9, 13], electrical engineering [10], and many other fields of the natural and social sciences. Certain sub-classes of these distributions are also referred to as a-stable, a-stable, stable Paretian dis- tributions, or Lévy alpha-stable distributions. Going forward, we will merely refer to them as stable distributions. The defining characteristic of random variables that follow stable laws is that the sum of two independent copies follows the same scaled and translated distribution [14].
    [Show full text]
  • Arxiv:2008.13704V3 [Astro-Ph.CO] 11 Dec 2020
    Bayesian analysis of LIGO-Virgo mergers: Primordial vs. astrophysical black hole populations Alex Hall ,1, ∗ Andrew D. Gow ,2 and Christian T. Byrnes 2 1Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, United Kingdom 2Department of Physics and Astronomy, University of Sussex, Brighton, BN1 9QH, United Kingdom We conduct a thorough Bayesian analysis of the possibility that the black hole merger events seen in gravitational waves are primordial black hole (PBH) mergers. Using the latest merger rate models for PBH binaries drawn from a lognormal mass function we compute posterior parameter constraints and Bayesian evidences using data from the first two observing runs of LIGO-Virgo. We account for theoretical uncertainty due to possible disruption of the binary by surrounding PBHs, which can suppress the merger rate significantly. We also consider simple astrophysically motivated models and find that these are favoured decisively over the PBH scenario, quantified by the Bayesian evidence ratio. Paying careful attention to the influence of the parameter priors and the quality of the model fits, we show that the evidence ratios can be understood by comparing the predicted chirp mass distribution to that of the data. We identify the posterior predictive distribution of chirp mass as a vital tool for discriminating between models. A model in which all mergers are PBH binaries is strongly disfavoured compared with astrophysical models, in part due to the over-prediction of heavy systems having Mchirp & 40 M and positive skewness over the range of observed masses which does not match the observations. We find that the fit is not significantly improved by adding a maximum mass cut-off, a bimodal mass function, or imposing that PBH binaries form at late times.
    [Show full text]