DOCSLIB.ORG

A Note on the Asymptotic Normality of Sample Autocorrelations for a Linear Stationary Sequence* Shuyuan He

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Note on the Asymptotic Normality of Sample Autocorrelations for a Linear Stationary Sequence* Shuyuan He Journal of Multivariate Analysis MV1618 journal of multivariate analysis 58, 182188 (1996) article no. 0046 A Note on the Asymptotic Normality of Sample Autocorrelations for a Linear Stationary Sequence* Shuyuan He Department of Probability and Statistics, Peking University, Beijing 100871, China We consider a stationary time series [Xt] given by Xt=k=& kZt&k, where [Zt] is a strictly stationary martingale difference white noise. Under assumptions { 2 that the spectral density f(*)of[Xt] is squared integrable and m |k|m k Ä0 for some {>1Â2, the asymptotic normality of the sample autocorrelations is shown. For a stationary long memory ARIMA( p, d, q) sequence, the condition { 2 m |k|m k Ä0 for some {>1Â2 is equivalent to the squared integrability of f(*). View metadata,This result citation extends and Theoremsimilar papers 4.2 of Cavazos-Cadenaat core.ac.uk [5], which were derived under brought to you by CORE 2 the condition m|k|m k Ä0. 1996 Academic Press, Inc. provided by Elsevier - Publisher Connector 1. INTRODUCTION Let [Xt] be a linear stationary time series given by 2 Xt= : kZt&k with 0< : k<, (1.1) k=& k=& 2 2 where [Zt] is a sequence of white noise: EZt=0, EZt =_ , EZt Zs=0 2 for t{s. Then [Xt] has the spectral density f(*)=(_ Â2?)|k=& k 2 2 _exp (ik*)| , the autocovariance function #(k)=_ j=& jj+k and the autocorrelation function \(k)=#(k)Â#(0). The sample autocovari- ance function based on X1 , X2 , ..., Xn is defined by #^ ( k )=#^ (&k)= n&k n t=1 (Xt+k&X n)(Xt&X n)Ân,0kn&1, with X n=t=1 XtÂn. The sample autocorrelation function is defined by \^ (k)=#^ ( k ) Â#^ (0), 1|k|n&1. Let brs= : [\(k+r)+\(k&r)&2\(r) \(k)] k=1 _[\(k+s)+\(k&s)&2\(s) \(k)]. (1.2) Received December 16, 1994 AMS 1991 subject classifications: 60B12; 62M10. Key words and phrases: autocorrelation, central limit theorem, martingale difference, ARIMA model. * Research partially supported by NSFC. 182 0047-259XÂ96 18.00 Copyright 1996 by Academic Press, Inc. All rights of reproduction in any form reserved. File: 683J 161801 . By:CV . Date:21:08:96 . Time:14:54 LOP8M. V8.0. Page 01:01 Codes: 4156 Signs: 1847 . Length: 50 pic 3 pts, 212 mm CLT OF SAMPLE AUTOCORRELATIONS 183 Suppose that f(*) is squared integrable and [Zt] is an independent and identically distributed (iid) white noise. Under the assumptions that 4 2 EZt < or m|k|m k Ä0, Cavazos-Cadena [5, Theorem 4.1, 4.2] proved, for each positive integer h, n1Â2(\^ (1)&\(1), \^ (2)&\(2), ..., \^ (h)&\(h))ON(0, B), as n Ä. (1.3) Where `` O '' means convergence in distribution and N(0, B) stands for the h-dimensional normal distribution with mean zero and covariance matrix B=(brs)h_h . Result (1.3) was established by Bartlett [3] under the condi- 4 tions: EZt < and k=& |k|<, and by Anderson and Walker [1] 2 under the conditions: k=& |k|< and k=& |k| k<. All their conditions imply the squared integrability of f(*). It is known that an equivalent condition for f(*) being squared 2 integrable is k=0 #k<. Another equivalent condition is brr<, for all r1 (see Theorem 2.1). From this point of view, we may not expect result (1.3) without the squared integrability of f(*). 2 The condition m|k|m k Ä0 is weaker than the condition used in Anderson and Walker [1], but does not include the stationary long memory (not the intermediate memory) sequence Xt=k=0 kZt&k defined by the ARIMA(0, d, 0) model d (1&B) Xt=Zt, t=0, 1, 2, ..., (1.4) where [Zt] is an iid white noise and d # (0, 0.25) (here, the condition d # (0, 0.25) ensures the squared integrability of f(*)). Indeed, the fact d&1 kCk for some constant C (see [4, pp. 467]) implies that lim m : 2C lim m x2d&2 dx=. (1.5) k | m Ä |k|m m Ä m In the following section we will prove (1.3) under conditions that [Zt] is a strictly stationary martingale difference white noise and for some {>1Â2, { 2 m : k Ä0, as m Ä , (1.6) |k|m and for a long memory ARIMA( p, d, q) (with d #(0,1Â2)) sequence, (1.6) is equivalent to the squared integrability of the spectral density f(*). 2. THE RESULTS AND PROOFS Theorem . 2 1 Let brs be defined by (1.2). Then the condition brr<, for all r1, implies the squared integrability of f(*). File: 683J 161802 . By:CV . Date:21:08:96 . Time:14:54 LOP8M. V8.0. Page 01:01 Codes: 2909 Signs: 1947 . Length: 45 pic 0 pts, 190 mm 184 SHUYUAN HE Proof. Using Bartlett's formula (see [2, pp. 489]), we have ? &2 2 2 brr=8?# (0) (\(r)&cos(r*)) f (*) d*, r1. (2.1) |0 &1 Let *0=cos \(1). From b11<, it is seen that we only need to prove 2 the integrability of f (*) in an open neighborhood of *0 . But this is ensured by the fact &1 |\(r)|=#0 : kk&r } k } =& &1 #0 : k k&r {}k r } 2 + : k k&r Ä0, as r Ä , } k r }= > 2 and cos(r*0)=cos((r&1) *0) cos(*0)&sin((r&1) *0) % _sin(*0) Ä0, as r Ä . Theorem . 2 2 Let [Xt] be defined by (1.1) with the spectral density f(*) being squared integrable and (1.6) being valid for some {>1Â2. Suppose that [Zt] is a strictly stationary martingale difference white noise with variance _2 and satisfies one of the following conditions: 2 2 (a) E(Zt | Zs; s<t)=_ a.s. for all t; 2 2 4 2 (b) [Zt] is ergodic, EZt Zs =_ for all s{t, and EZt Zt&j Zt&k=0 for any j, k0, j{k. Then for any positive integer h the result (1.3) is true. This theorem can be shown by following the procedure of proving Theorem 4.2 in Cavazos-Cadena [5], after proving (7.4) and (7.14) in that procedure via our Lemma 2.1 and Lemma 2.2. Lemma 2.1 Under the conditions of Theorem 2.2, for any positive integer h, 1Â2 n |D(n; h)| wÄp 0, as n Ä , (2.2) where ``wÄp '' means convergence in probability and n n 2 2 D(n; h)= : jj+h : Zt&jÂn& : Zt Ân . j \t t + =& =1 =1 File: 683J 161803 . By:CV . Date:21:08:96 . Time:14:54 LOP8M. V8.0. Page 01:01 Codes: 2519 Signs: 1230 . Length: 45 pic 0 pts, 190 mm CLT OF SAMPLE AUTOCORRELATIONS 185 2 Proof. Let aj=j j+k and !t=Zt . Then n n nD(n; h)= : aj : !t&j& : aj : !t= : anj !j , (2.3) j=& t=1 j=& t=1 j=& where n t=1 at&j&t=& at, for 1 j n, anj= n {t=1 at&j, otherwise. &k &k0 1&2 Let k0 be an integer such that 1&2 >0.5Â{. Define a(k)=n and { A( y)= : |jaj |Ây, B( y)=y : |aj| for y>0. (2.4) | j|y | j|y 2 We have limn a(k)=,1kk0. Using j=& |aj|j=& j < and Kronecker lemma (see [6, pp. 31]), we get A( y) Ä 0 and { 2 2 B( y)y |k|y (k+k+h)Ä0, as y Ä . Now, the fact that a(1)=n1Â2, n&1Â2a(k)=n1Â2&2&kn{(1&2&k+1)= { 1Â2 { a (k&1) and n a (k0) imply n 0 &1Â2 &1Â2 n : |anj |=n : |anj |+ : |anj |+ : |anj | j {j j j = =& =1 =& =1 &1Â2 1Â2 2n : |jaj |+2n : |aj| | j|n |j|>n &1Â2 &1Â2 2 n : | jaj |+n : | jaj | { 0<| j|a(1) a(1)<| j|a(2) &1Â2 &1Â2 +}}}+n : |jaj |+n a(k 0&1)<| j|a(k0) _ : |jaj |+B(n) a k j n = ( 0)<| | &1 &1Â2 2 a (1) : | jaj |+n a(2) : |aj | { j a a j a 0<| | (1) (1)<| | (2) &1Â2 1Â2 +}}}+n a(k0) : |aj|+n a(k0&1)<| j|a(k0) _ : |aj|+B(n) a k j n = ( 0)<| | 2[A(a(1))+B(a(1))+ } }} +B(a(k0&1)) +B(a(k0))+B(n)]Ä0, as n Ä . (2.5) 2 Now, (2.2) follows from Markov's inequality and E!t=_ . File: 683J 161804 . By:CV . Date:21:08:96 . Time:14:54 LOP8M. V8.0. Page 01:01 Codes: 2512 Signs: 983 . Length: 45 pic 0 pts, 190 mm 186 SHUYUAN HE Lemma 2.2. Under the conditions of Theorem 2.2, for any positive integer h, as n Ä (n) (n) (n) (W 1 , W 2 , ..., W h ) O (W1 , W2 , ..., Wh), as n Ä , (2.6) (n) &1Â2 n where W k =n j=1 ZjZj+kÂ_ and W1 , W2 , ..., Wh are iid random variables with common distribution N(0, 1). h Proof. For any real a1 , a2 , ..., ah , let Ut=j=1 ajZtZt&j. Then [Ut; t1] is a strictly stationary martingale difference with mean zero and 2 h 2 4 variance _u=j=1 aj _ . Let Ft=_[Zs; st] and U be the invariant - _ field of [Zt]. Then, U/Ft for all t. Hence, the ergodic theorem implies n 2 2 2 lim : E(Ut Ân | Ft&1)=E(Ut | U)=_u a.s., (2.7) n Ä t=1 and for any =>0, n 1 2 1Â2 lim : E(Ut I[|Ut |>n =] | Ft&1)=0 a.s.
Load more

Recommended publications
  • Power Comparisons of Five Most Commonly Used Autocorrelation Tests
    Pak.j.stat.oper.res. Vol.16 No. 1 2020 pp119-130 DOI: http://dx.doi.org/10.18187/pjsor.v16i1.2691 Pakistan Journal of Statistics and Operation Research Power Comparisons of Five Most Commonly Used Autocorrelation Tests Stanislaus S. Uyanto School of Economics and Business, Atma Jaya Catholic University of Indonesia, [email protected] Abstract In regression analysis, autocorrelation of the error terms violates the ordinary least squares assumption that the error terms are uncorrelated. The consequence is that the estimates of coefficients and their standard errors will be wrong if the autocorrelation is ignored. There are many tests for autocorrelation, we want to know which test is more powerful. We use Monte Carlo methods to compare the power of five most commonly used tests for autocorrelation, namely Durbin-Watson, Breusch-Godfrey, Box–Pierce, Ljung Box, and Runs tests in two different linear regression models. The results indicate the Durbin-Watson test performs better in the regression model without lagged dependent variable, although the advantage over the other tests reduce with increasing autocorrelation and sample sizes. For the model with lagged dependent variable, the Breusch-Godfrey test is generally superior to the other tests. Key Words: Correlated error terms; Ordinary least squares assumption; Residuals; Regression diagnostic; Lagged dependent variable. Mathematical Subject Classification: 62J05; 62J20. 1. Introduction An important assumption of the classical linear regression model states that there is no autocorrelation in the error terms. Autocorrelation of the error terms violates the ordinary least squares assumption that the error terms are uncorrelated, meaning that the Gauss Markov theorem (Plackett, 1949, 1950; Greene, 2018) does not apply, and that ordinary least squares estimators are no longer the best linear unbiased estimators.
    [Show full text]
  • LECTURES 2 - 3 : Stochastic Processes, Autocorrelation Function
    LECTURES 2 - 3 : Stochastic Processes, Autocorrelation function. Stationarity. Important points of Lecture 1: A time series fXtg is a series of observations taken sequentially over time: xt is an observation recorded at a specific time t. Characteristics of times series data: observations are dependent, become available at equally spaced time points and are time-ordered. This is a discrete time series. The purposes of time series analysis are to model and to predict or forecast future values of a series based on the history of that series. 2.2 Some descriptive techniques. (Based on [BD] x1.3 and x1.4) ......................................................................................... Take a step backwards: how do we describe a r.v. or a random vector? ² for a r.v. X: 2 d.f. FX (x) := P (X · x), mean ¹ = EX and variance σ = V ar(X). ² for a r.vector (X1;X2): joint d.f. FX1;X2 (x1; x2) := P (X1 · x1;X2 · x2), marginal d.f.FX1 (x1) := P (X1 · x1) ´ FX1;X2 (x1; 1) 2 2 mean vector (¹1; ¹2) = (EX1; EX2), variances σ1 = V ar(X1); σ2 = V ar(X2), and covariance Cov(X1;X2) = E(X1 ¡ ¹1)(X2 ¡ ¹2) ´ E(X1X2) ¡ ¹1¹2. Often we use correlation = normalized covariance: Cor(X1;X2) = Cov(X1;X2)=fσ1σ2g ......................................................................................... To describe a process X1;X2;::: we define (i) Def. Distribution function: (fi-di) d.f. Ft1:::tn (x1; : : : ; xn) = P (Xt1 · x1;:::;Xtn · xn); i.e. this is the joint d.f. for the vector (Xt1 ;:::;Xtn ). (ii) First- and Second-order moments. ² Mean: ¹X (t) = EXt 2 2 2 2 ² Variance: σX (t) = E(Xt ¡ ¹X (t)) ´ EXt ¡ ¹X (t) 1 ² Autocovariance function: γX (t; s) = Cov(Xt;Xs) = E[(Xt ¡ ¹X (t))(Xs ¡ ¹X (s))] ´ E(XtXs) ¡ ¹X (t)¹X (s) (Note: this is an infinite matrix).
    [Show full text]
  • Alternative Tests for Time Series Dependence Based on Autocorrelation Coefficients
    Alternative Tests for Time Series Dependence Based on Autocorrelation Coefficients Richard M. Levich and Rosario C. Rizzo * Current Draft: December 1998 Abstract: When autocorrelation is small, existing statistical techniques may not be powerful enough to reject the hypothesis that a series is free of autocorrelation. We propose two new and simple statistical tests (RHO and PHI) based on the unweighted sum of autocorrelation and partial autocorrelation coefficients. We analyze a set of simulated data to show the higher power of RHO and PHI in comparison to conventional tests for autocorrelation, especially in the presence of small but persistent autocorrelation. We show an application of our tests to data on currency futures to demonstrate their practical use. Finally, we indicate how our methodology could be used for a new class of time series models (the Generalized Autoregressive, or GAR models) that take into account the presence of small but persistent autocorrelation. _______________________________________________________________ An earlier version of this paper was presented at the Symposium on Global Integration and Competition, sponsored by the Center for Japan-U.S. Business and Economic Studies, Stern School of Business, New York University, March 27-28, 1997. We thank Paul Samuelson and the other participants at that conference for useful comments. * Stern School of Business, New York University and Research Department, Bank of Italy, respectively. 1 1. Introduction Economic time series are often characterized by positive
    [Show full text]
  • 3 Autocorrelation
    3 Autocorrelation Autocorrelation refers to the correlation of a time series with its own past and future values. Autocorrelation is also sometimes called “lagged correlation” or “serial correlation”, which refers to the correlation between members of a series of numbers arranged in time. Positive autocorrelation might be considered a specific form of “persistence”, a tendency for a system to remain in the same state from one observation to the next. For example, the likelihood of tomorrow being rainy is greater if today is rainy than if today is dry. Geophysical time series are frequently autocorrelated because of inertia or carryover processes in the physical system. For example, the slowly evolving and moving low pressure systems in the atmosphere might impart persistence to daily rainfall. Or the slow drainage of groundwater reserves might impart correlation to successive annual flows of a river. Or stored photosynthates might impart correlation to successive annual values of tree-ring indices. Autocorrelation complicates the application of statistical tests by reducing the effective sample size. Autocorrelation can also complicate the identification of significant covariance or correlation between time series (e.g., precipitation with a tree-ring series). Autocorrelation implies that a time series is predictable, probabilistically, as future values are correlated with current and past values. Three tools for assessing the autocorrelation of a time series are (1) the time series plot, (2) the lagged scatterplot, and (3) the autocorrelation function. 3.1 Time series plot Positively autocorrelated series are sometimes referred to as persistent because positive departures from the mean tend to be followed by positive depatures from the mean, and negative departures from the mean tend to be followed by negative departures (Figure 3.1).
    [Show full text]
  • Spatial Autocorrelation: Covariance and Semivariance Semivariance
    Spatial Autocorrelation: Covariance and Semivariancence Lily Housese ­P eters GEOG 593 November 10, 2009 Quantitative Terrain Descriptorsrs Covariance and Semivariogram areare numericc methods used to describe the character of the terrain (ex. Terrain roughness, irregularity) Terrain character has important implications for: 1. the type of sampling strategy chosen 2. estimating DTM accuracy (after sampling and reconstruction) Spatial Autocorrelationon The First Law of Geography ““ Everything is related to everything else, but near things are moo re related than distant things.” (Waldo Tobler) The value of a variable at one point in space is related to the value of that same variable in a nearby location Ex. Moranan ’s I, Gearyary ’s C, LISA Positive Spatial Autocorrelation (Neighbors are similar) Negative Spatial Autocorrelation (Neighbors are dissimilar) R(d) = correlation coefficient of all the points with horizontal interval (d) Covariance The degree of similarity between pairs of surface points The value of similarity is an indicator of the complexity of the terrain surface Smaller the similarity = more complex the terrain surface V = Variance calculated from all N points Cov (d) = Covariance of all points with horizontal interval d Z i = Height of point i M = average height of all points Z i+d = elevation of the point with an interval of d from i Semivariancee Expresses the degree of relationship between points on a surface Equal to half the variance of the differences between all possible points spaced a constant distance apart
    [Show full text]
  • Modeling and Generating Multivariate Time Series with Arbitrary Marginals Using a Vector Autoregressive Technique
    Modeling and Generating Multivariate Time Series with Arbitrary Marginals Using a Vector Autoregressive Technique Bahar Deler Barry L. Nelson Dept. of IE & MS, Northwestern University September 2000 Abstract We present a model for representing stationary multivariate time series with arbitrary mar- ginal distributions and autocorrelation structures and describe how to generate data quickly and accurately to drive computer simulations. The central idea is to transform a Gaussian vector autoregressive process into the desired multivariate time-series input process that we presume as having a VARTA (Vector-Autoregressive-To-Anything) distribution. We manipulate the correlation structure of the Gaussian vector autoregressive process so that we achieve the desired correlation structure for the simulation input process. For the purpose of computational efficiency, we provide a numerical method, which incorporates a numerical-search procedure and a numerical-integration technique, for solving this correlation-matching problem. Keywords: Computer simulation, vector autoregressive process, vector time series, multivariate input mod- eling, numerical integration. 1 1 Introduction Representing the uncertainty or randomness in a simulated system by an input model is one of the challenging problems in the practical application of computer simulation. There are an abundance of examples, from manufacturing to service applications, where input modeling is critical; e.g., modeling the time to failure for a machining process, the demand per unit time for inventory of a product, or the times between arrivals of calls to a call center. Further, building a large- scale discrete-event stochastic simulation model may require the development of a large number of, possibly multivariate, input models. Development of these models is facilitated by accurate and automated (or nearly automated) input modeling support.
    [Show full text]
  • The Probability Lifesaver: Order Statistics and the Median Theorem
    The Probability Lifesaver: Order Statistics and the Median Theorem Steven J. Miller December 30, 2015 Contents 1 Order Statistics and the Median Theorem 3 1.1 Definition of the Median 5 1.2 Order Statistics 10 1.3 Examples of Order Statistics 15 1.4 TheSampleDistributionoftheMedian 17 1.5 TechnicalboundsforproofofMedianTheorem 20 1.6 TheMedianofNormalRandomVariables 22 2 • Greetings again! In this supplemental chapter we develop the theory of order statistics in order to prove The Median Theorem. This is a beautiful result in its own, but also extremely important as a substitute for the Central Limit Theorem, and allows us to say non- trivial things when the CLT is unavailable. Chapter 1 Order Statistics and the Median Theorem The Central Limit Theorem is one of the gems of probability. It’s easy to use and its hypotheses are satisfied in a wealth of problems. Many courses build towards a proof of this beautiful and powerful result, as it truly is ‘central’ to the entire subject. Not to detract from the majesty of this wonderful result, however, what happens in those instances where it’s unavailable? For example, one of the key assumptions that must be met is that our random variables need to have finite higher moments, or at the very least a finite variance. What if we were to consider sums of Cauchy random variables? Is there anything we can say? This is not just a question of theoretical interest, of mathematicians generalizing for the sake of generalization. The following example from economics highlights why this chapter is more than just of theoretical interest.
    [Show full text]
  • Central Limit Theorem and Its Applications to Baseball
    Central Limit Theorem and Its Applications to Baseball by Nicole Anderson A project submitted to the Department of Mathematical Sciences in conformity with the requirements for Math 4301 (Honours Seminar) Lakehead University Thunder Bay, Ontario, Canada copyright c (2014) Nicole Anderson Abstract This honours project is on the Central Limit Theorem (CLT). The CLT is considered to be one of the most powerful theorems in all of statistics and probability. In probability theory, the CLT states that, given certain conditions, the sample mean of a sufficiently large number or iterates of independent random variables, each with a well-defined ex- pected value and well-defined variance, will be approximately normally distributed. In this project, a brief historical review of the CLT is provided, some basic concepts, two proofs of the CLT and several properties are discussed. As an application, we discuss how to use the CLT to study the sampling distribution of the sample mean and hypothesis testing using baseball statistics. i Acknowledgements I would like to thank my supervisor, Dr. Li, who helped me by sharing his knowledge and many resources to help make this paper come to life. I would also like to thank Dr. Adam Van Tuyl for all of his help with Latex, and support throughout this project. Thank you very much! ii Contents Abstract i Acknowledgements ii Chapter 1. Introduction 1 1. Historical Review of Central Limit Theorem 1 2. Central Limit Theorem in Practice 1 Chapter 2. Preliminaries 3 1. Definitions 3 2. Central Limit Theorem 7 Chapter 3. Proofs of Central Limit Theorem 8 1.
    [Show full text]
  • Random Processes
    Chapter 6 Random Processes Random Process • A random process is a time-varying function that assigns the outcome of a random experiment to each time instant: X(t). • For a fixed (sample path): a random process is a time varying function, e.g., a signal. – For fixed t: a random process is a random variable. • If one scans all possible outcomes of the underlying random experiment, we shall get an ensemble of signals. • Random Process can be continuous or discrete • Real random process also called stochastic process – Example: Noise source (Noise can often be modeled as a Gaussian random process. An Ensemble of Signals Remember: RV maps Events à Constants RP maps Events à f(t) RP: Discrete and Continuous The set of all possible sample functions {v(t, E i)} is called the ensemble and defines the random process v(t) that describes the noise source. Sample functions of a binary random process. RP Characterization • Random variables x 1 , x 2 , . , x n represent amplitudes of sample functions at t 5 t 1 , t 2 , . , t n . – A random process can, therefore, be viewed as a collection of an infinite number of random variables: RP Characterization – First Order • CDF • PDF • Mean • Mean-Square Statistics of a Random Process RP Characterization – Second Order • The first order does not provide sufficient information as to how rapidly the RP is changing as a function of timeà We use second order estimation RP Characterization – Second Order • The first order does not provide sufficient information as to how rapidly the RP is changing as a function
    [Show full text]
  • On the Use of the Autocorrelation and Covariance Methods for Feedforward Control of Transverse Angle and Position Jitter in Linear Particle Beam Accelerators*
    '^C 7 ON THE USE OF THE AUTOCORRELATION AND COVARIANCE METHODS FOR FEEDFORWARD CONTROL OF TRANSVERSE ANGLE AND POSITION JITTER IN LINEAR PARTICLE BEAM ACCELERATORS* Dean S. Ban- Advanced Photon Source, Argonne National Laboratory, 9700 S. Cass Ave., Argonne, IL 60439 ABSTRACT It is desired to design a predictive feedforward transverse jitter control system to control both angle and position jitter m pulsed linear accelerators. Such a system will increase the accuracy and bandwidth of correction over that of currently available feedback correction systems. Intrapulse correction is performed. An offline process actually "learns" the properties of the jitter, and uses these properties to apply correction to the beam. The correction weights calculated offline are downloaded to a real-time analog correction system between macropulses. Jitter data were taken at the Los Alamos National Laboratory (LANL) Ground Test Accelerator (GTA) telescope experiment at Argonne National Laboratory (ANL). The experiment consisted of the LANL telescope connected to the ANL ZGS proton source and linac. A simulation of the correction system using this data was shown to decrease the average rms jitter by a factor of two over that of a comparable standard feedback correction system. The system also improved the correction bandwidth. INTRODUCTION Figure 1 shows the standard setup for a feedforward transverse jitter control system. Note that one pickup #1 and two kickers are needed to correct beam position jitter, while two pickup #l's and one kicker are needed to correct beam trajectory-angle jitter. pickup #1 kicker pickup #2 Beam Fast loop Slow loop Processor Figure 1. Feedforward Transverse Jitter Control System It is assumed that the beam is fast enough to beat the correction signal (through the fast loop) to the kicker.
    [Show full text]
  • Lecture 4 Multivariate Normal Distribution and Multivariate CLT
    Lecture 4 Multivariate normal distribution and multivariate CLT. T We start with several simple observations. If X = (x1; : : : ; xk) is a k 1 random vector then its expectation is × T EX = (Ex1; : : : ; Exk) and its covariance matrix is Cov(X) = E(X EX)(X EX)T : − − Notice that a covariance matrix is always symmetric Cov(X)T = Cov(X) and nonnegative definite, i.e. for any k 1 vector a, × a T Cov(X)a = Ea T (X EX)(X EX)T a T = E a T (X EX) 2 0: − − j − j � We will often use that for any vector X its squared length can be written as X 2 = XT X: If we multiply a random k 1 vector X by a n k matrix A then the covariancej j of Y = AX is a n n matrix × × × Cov(Y ) = EA(X EX)(X EX)T AT = ACov(X)AT : − − T Multivariate normal distribution. Let us consider a k 1 vector g = (g1; : : : ; gk) of i.i.d. standard normal random variables. The covariance of g is,× obviously, a k k identity × matrix, Cov(g) = I: Given a n k matrix A, the covariance of Ag is a n n matrix × × � := Cov(Ag) = AIAT = AAT : Definition. The distribution of a vector Ag is called a (multivariate) normal distribution with covariance � and is denoted N(0; �): One can also shift this disrtibution, the distribution of Ag + a is called a normal distri­ bution with mean a and covariance � and is denoted N(a; �): There is one potential problem 23 with the above definition - we assume that the distribution depends only on covariance ma­ trix � and does not depend on the construction, i.e.
    [Show full text]
  • Lecture 10: Lagged Autocovariance and Correlation
    Lecture 10: Lagged autocovariance and correlation c Christopher S. Bretherton Winter 2014 Reference: Hartmann Atm S 552 notes, Chapter 6.1-2. 10.1 Lagged autocovariance and autocorrelation The lag-p autocovariance is defined N X ap = ujuj+p=N; (10.1.1) j=1 which measures how strongly a time series is related with itself p samples later or earlier. The zero-lag autocovariance a0 is equal to the power. Also note that ap = a−p because both correspond to a lag of p time samples. The lag-p autocorrelation is obtained by dividing the lag-p autocovariance by the variance: rp = ap=a0 (10.1.2) Many kinds of time series decorrelate in time so that rp ! 0 as p increases. On the other hand, for a purely periodic signal of period T , rp = cos(2πp∆t=T ) will oscillate between 1 at lags that are multiples of the period and -1 at lags that are odd half-multiples of the period. Thus, if the autocorrelation drops substantially below zero at some lag P , that usually corresponds to a preferred peak in the spectral power at periods around 2P . More generally, the autocovariance sequence (a0; a1; a2;:::) is intimately re- lated to the power spectrum. Let S be the power spectrum deduced from the DFT, with components 2 2 Sm = ju^mj =N ; m = 1; 2;:::;N (10.1.3) Then one can show using a variant of Parseval's theorem that the IDFT of the power spectrum gives the lagged autocovariance sequence. Specif- ically, let the vector a be the lagged autocovariance sequence (acvs) fap−1; p = 1;:::;Ng computed in the usual DFT style by interpreting indices j + p outside the range 1; ::; N periodically: j + p mod(j + p − 1;N) + 1 (10.1.4) 1 Amath 482/582 Lecture 10 Bretherton - Winter 2014 2 Note that because of the periodicity an−1 = a−1, i.
    [Show full text]