DOCSLIB.ORG

Towards an Explanatory Model for Network Traffic

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Towards an Explanatory Model for Network Traffic Towards An Explanatory Model for Network Traffic Jorge Gonzalez Joshua Clymer Chad Bollmann Dept. of Mathematical Sciences SEAP Intern Dept. of Electrical & Computer Engineering Florida Atlantic University Naval Postgraduate School Naval Postgraduate School Boca Raton, FL, USA Monterey, CA, USA Monterey, CA, USA [email protected] [email protected] Abstract—This work presents two explanatory mathematical tailed distributions per the Generalized Central Limit Theorem models explaining how network traffic features that display Gaus- (GCLT). sian tendencies in single devices and small networks aggregate The primary contribution of this work is outlining our to alpha-stable processes in larger networks. The first model shows how self-similarity originates from an impulsive-noise- explanation of device traffic aggregation to an alpha-stable based representation of individual processes. A second model or Gaussian result. We use this theory to develop a basic uses renewal processes to justify impulsive process aggregation aggregation model that leads to alpha-stable distributed traffic. to alpha-stable or Gaussian end states and permits estimating network traffic alpha-stable rates of convergence. We develop II. THE IMPULSE MODEL a model based on this first method to empirically validate this aggregation approach. The goal of this model is to explain the aggregation of traffic Index Terms—alpha-stable, computer network traffic model, by looking inside the sub-windows where aggregations occur. heavy-tail, self-similar We define an impulse as a group of time stamps (packets) within a sub-window that are related by a unique source and I. INTRODUCTION destination IP pair (ip0; ip1). Different impulses are assumed We propose explanatory, complementary models for ag- to be independent. gregated network traffic, starting from the device level. Our Impulses are ordered in such a way that P(Yi = a) does purpose is to understand how network traffic aggregates from not depend on i, where Yi is the volume of the ith impulse. individual sources and investigate the tendency of certain The total volume of traffic in a sub-window is given by network traffic features including packet rate to trend towards the sum of the impulses within it, the number of which is alpha-stable [1], [2] in larger networks, while the same features described by a distribution E. V denotes the distribution of exhibit non-parametric or Gaussian distributions in smaller the volumes of all impulses. Traffic in a generic sub-window networks. is given by S = Y1 + Y2 + ::: + Ye, where e sampled The central theme of our approach is an impulse-based from E. The Yi’s are assumed to be independent since they model of traffic from individual processes on a single device. represent the volume of different processes within a sub- We observe that these traffic processes frequently are relatively window and are also identically distributed by construction. periodic and consistent in terms of volume, as illustrated in Figure 1, a rate plot of traffic to a single, centrally- managed device. The impulse characterization of traffic is 9000 justified by the fact that even long transmissions (e.g., a video on YouTube) are sent as short bursts of traffic rather than 7500 constant-rate, lengthier transmissions. Intuitively, the aggregation and perturbation of these traffic 6000 impulses from hundreds of devices leads to the frequently- observed self-similarity in aggregated network traces [3], [4]. Rate [packets/s] Rate 4500 Present throughout the trace: Music Buffering These models have historically relied on Levy´ processes Network and Windows Backups 3600-5200 pps 80, 200 pps fXt : t ≥ 0g, including Poisson and fractional Brownian motion [5]; we propose an alternative Levy´ process, the alpha- 3000 stable, due to its deep theoretical connections with well- YouTube Buffering 600 - 1200 pps Local Routines documented characteristics of traffic. More specifically, Levy´ 1500 1200 and 200 pps processes are SS if and only if they are alpha-stable [6] and are the domain of attraction of random variables with heavy- 0 750 900 0 150 300 Time [s] 450 600 This work was funded by the NPS Naval Research Program under proposal NRP-18-039A. Joshua Clymer is with the ASEE Science and Engineering Figure 1. Rate plot of several minutes of traffic received at local host with Apprenticeship Program. periods of music and video streaming. Their common distribution is approximated by V . We provide and inactivity periods are shadowed by Power distributions empirical evidence that V is well approximated by heavy- for a considerably long period of time, nevertheless a shard tailed functions belonging to the domain of attraction of alpha deviation from this trend is clearly expected at some point. stable distributions. The asymptotic behavior of the aggregation S can now be studied using the GCLT. Specifically, we can estimate the convergence rate under the stronger assumption that Y1 lies Activity Periods in the strong domain of attraction of a stable distribution [7]. 102 Inactivity Periods The convergence rate serves to estimate the expected error 101 in the stable fits. 0 A. Aggregation from sub-windows to window: Ergodicity 10 Intuitively, we think of a window as a set of consecutive Frequency 10−1 samples of sub-windows (typically between 600 and 1000). −2 The distribution of the aggregation of the random variables 10 defined above determines the outcome of randomly selecting 10−3 sub-windows within a stationary trace. 10−2 10−1 100 When interpreted as a Bernoulli scheme the model inherits Duration (seconds) the stronger condition of ergodicity, which implies that the dis- tribution of a large enough window approximates the sample Figure 2. The probability distributions of packet flow duration and the distribution of the aggregation S. interruption periods between packet flows related to the same process. Although the classical argument using moments is unavail- able, we can justify this convergence using several other A packet flow is a string of packets related by (ip0; ip1) methods. The property is also demonstrated empirically in the possibly extending several sub-windows (i.e a consecutive longer manuscript. group of impulses). Two packets belong to the same flow if they are less than two sub-windows apart. We also define the B. Aggregation using Renewal processes random variables Sk and Ek given by We can also model traffic as the aggregation of renewal k−1 k−1 processes per Taqqu and Levy´ in [8] Specifically, they look at X X S = S + U + F k ≥ 1 processes of the form k 0 j j j=0 j=0 (2) T M ∗ X X Ek = Sk + Uk k ≥ 0 X (T;M) = Xm;t (1) t=1 m=1 representing the start-time and end-time of a packet flow respectively. Ik = (Sk;Ek] denotes the kth ON interval. where for each t, the random variables Xm;t : 1 ≤ m ≤ M are i.i.d. copies of a renewal process, and are interpreted as as a set Finally, we define the random variables of similar processes (say streaming from many different users), ( 1 wk \ Ik 6= ; whereas we interpret the index t as ranging across different δk = 0 otherwise processes or network activities in the distribution sense. ∗ The accumulation X (T;M) approaches a Gaussian frac- where wk refers to the kth sub-window in the trace. tional Brownian motion when T << M, and a stable process A signal is now expressed as when T >> M. See [8] for a quick note on how these two 1 self-similar processes differ. We can assume that a certain X X = W δ (3) process is identically distributed across devices if we restrict t k k k=0 ourselves to relatively short windows. PT For a given k ≥ 0 we think of Wk as an independent copy We now interpret the expression t=1 Xt as the superposi- of the random variable of number of packets over time rates tion of the volume of several processes at a sub-window. The with common distribution R, which we now assume to be sum of m copies of X(t) in a given sub-window suggests the truncated (Wk are assumed to posses finite second moments). traffic of m ”similar” processes. We expect that the relation Uk represents an independent copy of the packet flow duration T >> M is satisfied in large networks and in traces captured with distribution U and at busy nodes due to the increased effect of noise due to similarly we now introduce Fk denotes the OFF period the multi-tasking nature of a device using multiple network duration with distribution F . Both Uk and Fk will be assumed sockets. to satisfy the same conditions on Uk as in [8], namely they are i.i.d. and have finite variance or belong to the domain of C. Simulations attraction of a stable distribution with 1 ≤ α ≤ 2. In addition, These simple models capture some of the main properties Wk is independent of Uk and Fk. Figure 2 shows how activity of real network traffic which depends on a great number of hard to quantify factors by describing it in the following way: 1 d ks distance =0.053 Real Traffic(x1; :::) = Toy Model1(V; E) + error1 0.5 Empirical CDF d Simulated CDF = Toy Model (R; T; M; U; F ) + error2 0 2 200 300 400 500 600 700 800 900 1000 1100 1200 1 where both error1 and error1 go to zero asymptotically. Notice ks distance = 0.095 that these models are very much related. We think of them in 0.5 Empirical CDF Simulated CDF 0 terms of the relation: 100 200 300 400 500 600 700 800 900 1000 d 1 Toy Model2(R; T; M; U; F ) = Toy Model1(V; E)+error(U; F ): ks distance = 0.104 0.5 Empirical CDF Model 2 is analogous to the M=G=1 construction due to Cumulative Probability Simulated CDF 0 Cox .
Load more

Recommended publications
  • Introduction to Lévy Processes
    Introduction to Lévy Processes Huang Lorick [email protected] Document type These are lecture notes. Typos, errors, and imprecisions are expected. Comments are welcome! This version is available at http://perso.math.univ-toulouse.fr/lhuang/enseignements/ Year of publication 2021 Terms of use This work is licensed under a Creative Commons Attribution 4.0 International license: https://creativecommons.org/licenses/by/4.0/ Contents Contents 1 1 Introduction and Examples 2 1.1 Infinitely divisible distributions . 2 1.2 Examples of infinitely divisible distributions . 2 1.3 The Lévy Khintchine formula . 4 1.4 Digression on Relativity . 6 2 Lévy processes 8 2.1 Definition of a Lévy process . 8 2.2 Examples of Lévy processes . 9 2.3 Exploring the Jumps of a Lévy Process . 11 3 Proof of the Levy Khintchine formula 19 3.1 The Lévy-Itô Decomposition . 19 3.2 Consequences of the Lévy-Itô Decomposition . 21 3.3 Exercises . 23 3.4 Discussion . 23 4 Lévy processes as Markov Processes 24 4.1 Properties of the Semi-group . 24 4.2 The Generator . 26 4.3 Recurrence and Transience . 28 4.4 Fractional Derivatives . 29 5 Elements of Stochastic Calculus with Jumps 31 5.1 Example of Use in Applications . 31 5.2 Stochastic Integration . 32 5.3 Construction of the Stochastic Integral . 33 5.4 Quadratic Variation and Itô Formula with jumps . 34 5.5 Stochastic Differential Equation . 35 Bibliography 38 1 Chapter 1 Introduction and Examples In this introductive chapter, we start by defining the notion of infinitely divisible distributions. We then give examples of such distributions and end this chapter by stating the celebrated Lévy-Khintchine formula.
    [Show full text]
  • A Mathematical Theory of Network Interference and Its Applications
    INVITED PAPER A Mathematical Theory of Network Interference and Its Applications A unifying framework is developed to characterize the aggregate interference in wireless networks, and several applications are presented. By Moe Z. Win, Fellow IEEE, Pedro C. Pinto, Student Member IEEE, and Lawrence A. Shepp ABSTRACT | In this paper, we introduce a mathematical I. INTRODUCTION framework for the characterization of network interference in In a wireless network composed of many spatially wireless systems. We consider a network in which the scattered nodes, communication is constrained by various interferers are scattered according to a spatial Poisson process impairments such as the wireless propagation effects, and are operating asynchronously in a wireless environment network interference, and thermal noise. The effects subject to path loss, shadowing, and multipath fading. We start introduced by propagation in the wireless channel include by determining the statistical distribution of the aggregate the attenuation of radiated signals with distance (path network interference. We then investigate four applications of loss), the blocking of signals caused by large obstacles the proposed model: 1) interference in cognitive radio net- (shadowing), and the reception of multiple copies of the works; 2) interference in wireless packet networks; 3) spectrum same transmitted signal (multipath fading). The network of the aggregate radio-frequency emission of wireless net- interference is due to accumulation of signals radiated by works; and 4) coexistence between ultrawideband and nar- other transmitters, which undesirably affect receiver nodes rowband systems. Our framework accounts for all the essential in the network. The thermal noise is introduced by the physical parameters that affect network interference, such as receiver electronics and is usually modeled as additive the wireless propagation effects, the transmission technology, white Gaussian noise (AWGN).
    [Show full text]
  • Introduction to Lévy Processes
    Introduction to L´evyprocesses Graduate lecture 22 January 2004 Matthias Winkel Departmental lecturer (Institute of Actuaries and Aon lecturer in Statistics) 1. Random walks and continuous-time limits 2. Examples 3. Classification and construction of L´evy processes 4. Examples 5. Poisson point processes and simulation 1 1. Random walks and continuous-time limits 4 Definition 1 Let Yk, k ≥ 1, be i.i.d. Then n X 0 Sn = Yk, n ∈ N, k=1 is called a random walk. -4 0 8 16 Random walks have stationary and independent increments Yk = Sk − Sk−1, k ≥ 1. Stationarity means the Yk have identical distribution. Definition 2 A right-continuous process Xt, t ∈ R+, with stationary independent increments is called L´evy process. 2 Page 1 What are Sn, n ≥ 0, and Xt, t ≥ 0? Stochastic processes; mathematical objects, well-defined, with many nice properties that can be studied. If you don’t like this, think of a model for a stock price evolving with time. There are also many other applications. If you worry about negative values, think of log’s of prices. What does Definition 2 mean? Increments , = 1 , are independent and Xtk − Xtk−1 k , . , n , = 1 for all 0 = . Xtk − Xtk−1 ∼ Xtk−tk−1 k , . , n t0 < . < tn Right-continuity refers to the sample paths (realisations). 3 Can we obtain L´evyprocesses from random walks? What happens e.g. if we let the time unit tend to zero, i.e. take a more and more remote look at our random walk? If we focus at a fixed time, 1 say, and speed up the process so as to make n steps per time unit, we know what happens, the answer is given by the Central Limit Theorem: 2 Theorem 1 (Lindeberg-L´evy) If σ = V ar(Y1) < ∞, then Sn − (Sn) √E → Z ∼ N(0, σ2) in distribution, as n → ∞.
    [Show full text]
  • Fclts for the Quadratic Variation of a CTRW and for Certain Stochastic Integrals
    FCLTs for the Quadratic Variation of a CTRW and for certain stochastic integrals No`eliaViles Cuadros (joint work with Enrico Scalas) Universitat de Barcelona Sevilla, 17 de Septiembre 2013 Damped harmonic oscillator subject to a random force The equation of motion is informally given by x¨(t) + γx_(t) + kx(t) = ξ(t); (1) where x(t) is the position of the oscillating particle with unit mass at time t, γ > 0 is the damping coefficient, k > 0 is the spring constant and ξ(t) represents white L´evynoise. I. M. Sokolov, Harmonic oscillator under L´evynoise: Unexpected properties in the phase space. Phys. Rev. E. Stat. Nonlin Soft Matter Phys 83, 041118 (2011). 2 of 27 The formal solution is Z t x(t) = F (t) + G(t − t0)ξ(t0)dt0; (2) −∞ where G(t) is the Green function for the homogeneous equation. The solution for the velocity component can be written as Z t 0 0 0 v(t) = Fv (t) + Gv (t − t )ξ(t )dt ; (3) −∞ d d where Fv (t) = dt F (t) and Gv (t) = dt G(t). 3 of 27 • Replace the white noise with a sequence of instantaneous shots of random amplitude at random times. • They can be expressed in terms of the formal derivative of compound renewal process, a random walk subordinated to a counting process called continuous-time random walk. A continuous time random walk (CTRW) is a pure jump process given by a sum of i.i.d. random jumps fYi gi2N separated by i.i.d. random waiting times (positive random variables) fJi gi2N.
    [Show full text]
  • Levy Processes
    LÉVY PROCESSES, STABLE PROCESSES, AND SUBORDINATORS STEVEN P.LALLEY 1. DEFINITIONSAND EXAMPLES d Definition 1.1. A continuous–time process Xt = X(t ) t 0 with values in R (or, more generally, in an abelian topological groupG ) isf called a Lévyg ≥ process if (1) its sample paths are right-continuous and have left limits at every time point t , and (2) it has stationary, independent increments, that is: (a) For all 0 = t0 < t1 < < tk , the increments X(ti ) X(ti 1) are independent. − (b) For all 0 s t the··· random variables X(t ) X−(s ) and X(t s ) X(0) have the same distribution.≤ ≤ − − − The default initial condition is X0 = 0. A subordinator is a real-valued Lévy process with nondecreasing sample paths. A stable process is a real-valued Lévy process Xt t 0 with ≥ initial value X0 = 0 that satisfies the self-similarity property f g 1/α (1.1) Xt =t =D X1 t > 0. 8 The parameter α is called the exponent of the process. Example 1.1. The most fundamental Lévy processes are the Wiener process and the Poisson process. The Poisson process is a subordinator, but is not stable; the Wiener process is stable, with exponent α = 2. Any linear combination of independent Lévy processes is again a Lévy process, so, for instance, if the Wiener process Wt and the Poisson process Nt are independent then Wt Nt is a Lévy process. More important, linear combinations of independent Poisson− processes are Lévy processes: these are special cases of what are called compound Poisson processes: see sec.
    [Show full text]
  • Financial Modeling with L´Evy Processes and Applying L
    FINANCIAL MODELING WITH LEVY´ PROCESSES AND APPLYING LEVY´ SUBORDINATOR TO CURRENT STOCK DATA by GONSALGE ALMEIDA Submitted in partial fullfillment of the requirements for the degree of Doctor of Philosophy Dissertation Advisor: Dr. Wojbor A. Woyczynski Department of Mathematics, Applied Mathematics and Statistics CASE WESTERN RESERVE UNIVERSITY January 2020 CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES We hereby approve the dissertation of Gonsalge Almeida candidate for the Doctoral of Philosophy degree Committee Chair: Dr.Wojbor Woyczynski Professor, Department of the Mathematics, Applied Mathematics and Statis- tics Committee: Dr.Alethea Barbaro Associate Professor, Department of the Mathematics, Applied Mathematics and Statistics Committee: Dr.Jenny Brynjarsdottir Associate Professor, Department of the Mathematics, Applied Mathematics and Statistics Committee: Dr.Peter Ritchken Professor, Weatherhead School of Management Acceptance date: June 14, 2019 *We also certify that written approval has been obtained for any proprietary material contained therein. CONTENTS List of Figures iv List of Tables ix Introduction . .1 1 Financial Modeling with L´evyProcesses and Infinitely Divisible Distributions 5 1.1 Introduction . .5 1.2 Preliminaries on L´evyprocesses . .6 1.3 Characteristic Functions . .8 1.4 Cumulant Generating Function . .9 1.5 α−Stable Distributions . 10 1.6 Tempered Stable Distribution and Process . 19 1.6.1 Tempered Stable Diffusion and Super-Diffusion . 23 1.7 Numerical Approximation of Stable and Tempered Stable Sample Paths 28 1.8 Monte Carlo Simulation for Tempered α−Stable L´evyprocess . 34 2 Brownian Subordination (Tempered Stable Subordinator) 44 i 2.1 Introduction . 44 2.2 Tempered Anomalous Subdiffusion . 46 2.3 Subordinators . 49 2.4 Time-Changed Brownian Motion .
    [Show full text]
  • Models Beyond the Dirichlet Process
    Models beyond the Dirichlet process Antonio Lijoi Igor Prünster No. 129 December 2009 www.carloalberto.org/working_papers © 2009 by Antonio Lijoi and Igor Prünster. Any opinions expressed here are those of the authors and not those of the Collegio Carlo Alberto. Models beyond the Dirichlet process Antonio Lijoi1 and Igor Prunster¨ 2 1 Dipartimento Economia Politica e Metodi Quantitatavi, Universit`adegli Studi di Pavia, via San Felice 5, 27100 Pavia, Italy and CNR{IMATI, via Bassini 15, 20133 Milano. E-mail: [email protected] 2 Dipartimento di Statistica e Matematica Applicata, Collegio Carlo Alberto and ICER, Universit`a degli Studi di Torino, Corso Unione Sovietica 218/bis, 10134 Torino, Italy. E-mail: [email protected] September 2009 Abstract. Bayesian nonparametric inference is a relatively young area of research and it has recently undergone a strong development. Most of its success can be explained by the considerable degree of flexibility it ensures in statistical modelling, if compared to parametric alternatives, and by the emergence of new and efficient simulation techniques that make nonparametric models amenable to concrete use in a number of applied sta- tistical problems. Since its introduction in 1973 by T.S. Ferguson, the Dirichlet process has emerged as a cornerstone in Bayesian nonparametrics. Nonetheless, in some cases of interest for statistical applications the Dirichlet process is not an adequate prior choice and alternative nonparametric models need to be devised. In this paper we provide a review of Bayesian nonparametric models that go beyond the Dirichlet process. 1 Introduction Bayesian nonparametric inference is a relatively young area of research and it has recently under- gone a strong development.
    [Show full text]
  • Final Report (PDF)
    Foundation of Stochastic Analysis Krzysztof Burdzy (University of Washington) Zhen-Qing Chen (University of Washington) Takashi Kumagai (Kyoto University) September 18-23, 2011 1 Scientific agenda of the conference Over the years, the foundations of stochastic analysis included various specific topics, such as the general theory of Markov processes, the general theory of stochastic integration, the theory of martingales, Malli- avin calculus, the martingale-problem approach to Markov processes, and the Dirichlet form approach to Markov processes. To create some focus for the very broad topic of the conference, we chose a few areas of concentration, including • Dirichlet forms • Analysis on fractals and percolation clusters • Jump type processes • Stochastic partial differential equations and measure-valued processes Dirichlet form theory provides a powerful tool that connects the probabilistic potential theory and ana- lytic potential theory. Recently Dirichlet forms found its use in effective study of fine properties of Markov processes on spaces with minimal smoothness, such as reflecting Brownian motion on non-smooth domains, Brownian motion and jump type processes on Euclidean spaces and fractals, and Markov processes on trees and graphs. It has been shown that Dirichlet form theory is an important tool in study of various invariance principles, such as the invariance principle for reflected Brownian motion on domains with non necessarily smooth boundaries and the invariance principle for Metropolis algorithm. Dirichlet form theory can also be used to study a certain type of SPDEs. Fractals are used as an approximation of disordered media. The analysis on fractals is motivated by the desire to understand properties of natural phenomena such as polymers, and growth of molds and crystals.
    [Show full text]
  • Modern Bayesian Nonparametrics
    Modern Bayesian Nonparametrics Peter Orbanz Yee Whye Teh Cambridge University and Columbia University Gatsby Computational Neuroscience Unit, UCL NIPS 2011 Peter Orbanz & Yee Whye Teh 1 / 72 OVERVIEW 1. Nonparametric Bayesian models 2. Regression 3. Clustering 4. Applications Coffee refill break 5. Asymptotics 6. Exchangeability 7. Latent feature models 8. Dirichlet process 9. Completely random measures 10. Summary Peter Orbanz & Yee Whye Teh 2 / 72 C. E. Rasmussen & C. K. I. Williams, Gaussian Processes for Machine Learning, the MIT Press, 2006, ISBN 026218253X. c 2006 Massachusetts Institute of Technology. www.GaussianProcess.org/gpml 20 Regression 3 PARAMETERS AND PATTERNS2 1 0 Parameters output, y −1 −2 P(Xjθ) =−3 Probability[datajpattern] −5 0 5 input, x (a), =1 3 3 2 2 1 1 0 0 output, y −1 output, y −1 −2 −2 −3 −3 −5 0 5 −5 0 5 input, x input, x (b), =0.3 (c), =3 Figure 2.5: (a) Data is generated from a GP with hyperparameters (, σf , σn)= Inference idea(1, 1, 0.1), as shown by the + symbols. Using Gaussian process prediction with these hyperparameters we obtain a 95% confidence region for the underlying function f (shown in grey). Panels (b) and (c) again show the 95% confidence region, but this timedata for hyperparameter = underlying values pattern (0.3, 1.08, 0 +.00005) independent and (3.0, 1.16, noise0.89) respectively. The covariance is denoted ky as it is for the noisy targets y rather than for the 2 Peter Orbanz & Yee Whye Teh underlying function f. Observe that the length-scale , the signal variance σf 3 / 72 2 hyperparameters and the noise variance σn can be varied.
    [Show full text]
  • Multifractal Analysis of Superprocesses with Stable
    The Annals of Probability 2015, Vol. 43, No. 5, 2763–2809 DOI: 10.1214/14-AOP951 c Institute of Mathematical Statistics, 2015 MULTIFRACTAL ANALYSIS OF SUPERPROCESSES WITH STABLE BRANCHING IN DIMENSION ONE By Leonid Mytnik1 and Vitali Wachtel2 Technion—Israel Institute of Technology and University of Munich We show that density functions of a (α, 1, β)-superprocesses are almost sure multifractal for α > β + 1, β ∈ (0, 1) and calculate the corresponding spectrum of singularities. 1. Introduction, main results and discussion. For 0 < α ≤ 2 and 1+ β ∈ d (1, 2], the so-called (α, d, β)-superprocess X = {Xt : t ≥ 0} in R is a finite measure-valued process related to the log-Laplace equation d (1.1) u =∆ u + au − bu1+β, dt α where a ∈ R and b> 0 are any fixed constants. Its underlying motion is de- α/2 scribed by the fractional Laplacian ∆α := −(−∆) determining a symmet- ric α-stable motion in Rd of index α ∈ (0, 2] (Brownian motion corresponds to α = 2), whereas its continuous-state branching mechanism (1.2) v 7→ −av + bv1+β, v ≥ 0, belongs to the domain of attraction of a stable law of index 1 + β ∈ (1, 2] (the branching mechanism is critical if a = 0). α Let d< β . Then, for any fixed time t, Xt(dx) is a.s. absolutely continuous with respect to the Lebesgue measure (cf. Fleischmann [3] for a = 0). In the case of β = 1, there is a continuous version of the density of Xt(dx) for all α> 1; see Konno and Shiga [12].
    [Show full text]
  • BEJ 22 4.Pdf
    Official Journal of the Bernoulli Society for Mathematical Statistics and Probability Volume Twenty Two Number Four November 2016 ISSN: 1350-7265 CONTENTS Papers MALLER, R.A. 1963 Conditions for a Lévy process to stay positive near 0, in probability LACAUX, C. and SAMORODNITSKY, G. 1979 Time-changed extremal process as a random sup measure BISCIO, C.A.N. and LAVANCIER, F. 2001 Quantifying repulsiveness of determinantal point processes SHAO, Q.-M. and ZHOU, W.-X. 2029 Cramér type moderate deviation theorems for self-normalized processes WANG, M. and MARUYAMA, Y. 2080 Consistency of Bayes factor for nonnested model selection when the model dimension grows LANCONELLI, A. and STAN, A.I. 2101 A note on a local limit theorem for Wiener space valued random variables HUCKEMANN, S., KIM, K.-R., MUNK, A., REHFELDT, F., 2113 SOMMERFELD, M., WEICKERT, J. and WOLLNIK, C. The circular SiZer, inferred persistence of shape parameters and application to early stem cell differentiation DÖRING, H., FARAUD, G. and KÖNIG, W. 2143 Connection times in large ad-hoc mobile networks DELYON, B. and PORTIER, F. 2177 Integral approximation by kernel smoothing CORTINES, A. 2209 The genealogy of a solvable population model under selection with dynamics related to directed polymers ARNAUDON, M. and MICLO, L. 2237 A stochastic algorithm finding p-means on the circle HEAUKULANI, C. and ROY, D.M. 2301 The combinatorial structure of beta negative binomial processes (continued) The papers published in Bernoulli are indexed or abstracted in Current Index to Statistics, Mathematical Reviews, Statistical Theory and Method Abstracts-Zentralblatt (STMA-Z), and Zentralblatt für Mathematik (also avalaible on the MATH via STN database and Compact MATH CD-ROM).
    [Show full text]
  • Random Walks at Random Times: Convergence to Iterated Lévy
    The Annals of Probability 2013, Vol. 41, No. 4, 2682–2708 DOI: 10.1214/12-AOP770 c Institute of Mathematical Statistics, 2013 RANDOM WALKS AT RANDOM TIMES: CONVERGENCE TO ITERATED LEVY´ MOTION, FRACTIONAL STABLE MOTIONS, AND OTHER SELF-SIMILAR PROCESSES By Paul Jung1 and Greg Markowsky2 University of Alabama at Birmingham and Monash University For a random walk defined for a doubly infinite sequence of times, we let the time parameter itself be an integer-valued process, and call the orginal process a random walk at random time. We find the scaling limit which generalizes the so-called iterated Brownian motion. Khoshnevisan and Lewis [Ann. Appl. Probab. 9 (1999) 629–667] suggested “the existence of a form of measure-theoretic duality” be- tween iterated Brownian motion and a Brownian motion in random scenery. We show that a random walk at random time can be consid- ered a random walk in “alternating” scenery, thus hinting at a mech- anism behind this duality. Following Cohen and Samorodnitsky [Ann. Appl. Probab. 16 (2006) 1432–1461], we also consider alternating random reward schema asso- ciated to random walks at random times. Whereas random reward schema scale to local time fractional stable motions, we show that the alternating random reward schema scale to indicator fractional stable motions. Finally, we show that one may recursively “subordinate” random time processes to get new local time and indicator fractional stable motions and new stable processes in random scenery or at random times. When α = 2, the fractional stable motions given by the recur- sion are fractional Brownian motions with dyadic H ∈ (0, 1).
    [Show full text]