Autocovariance Function Estimation Via Penalized Regression
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Stationary Processes and Their Statistical Properties
Stationary Processes and Their Statistical Properties Brian Borchers March 29, 2001 1 Stationary processes A discrete time stochastic process is a sequence of random variables Z1, Z2, :::. In practice we will typically analyze a single realization z1, z2, :::, zn of the stochastic process and attempt to esimate the statistical properties of the stochastic process from the realization. We will also consider the problem of predicting zn+1 from the previous elements of the sequence. We will begin by focusing on the very important class of stationary stochas- tic processes. A stochastic process is strictly stationary if its statistical prop- erties are unaffected by shifting the stochastic process in time. In particular, this means that if we take a subsequence Zk+1, :::, Zk+m, then the joint distribution of the m random variables will be the same no matter what k is. Stationarity requires that the mean of the stochastic process be a constant. E[Zk] = µ. and that the variance is constant 2 V ar[Zk] = σZ : Also, stationarity requires that the covariance of two elements separated by a distance m is constant. That is, Cov(Zk;Zk+m) is constant. This covariance is called the autocovariance at lag m, and we will use the notation γm. Since Cov(Zk;Zk+m) = Cov(Zk+m;Zk), we need only find γm for m 0. The ≥ correlation of Zk and Zk+m is the autocorrelation at lag m. We will use the notation ρm for the autocorrelation. It is easy to show that γk ρk = : γ0 1 2 The autocovariance and autocorrelation ma- trices The covariance matrix for the random variables Z1, :::, Zn is called an auto- covariance matrix. -
Stochastic Process - Introduction
Stochastic Process - Introduction • Stochastic processes are processes that proceed randomly in time. • Rather than consider fixed random variables X, Y, etc. or even sequences of i.i.d random variables, we consider sequences X0, X1, X2, …. Where Xt represent some random quantity at time t. • In general, the value Xt might depend on the quantity Xt-1 at time t-1, or even the value Xs for other times s < t. • Example: simple random walk . week 2 1 Stochastic Process - Definition • A stochastic process is a family of time indexed random variables Xt where t belongs to an index set. Formal notation, { t : ∈ ItX } where I is an index set that is a subset of R. • Examples of index sets: 1) I = (-∞, ∞) or I = [0, ∞]. In this case Xt is a continuous time stochastic process. 2) I = {0, ±1, ±2, ….} or I = {0, 1, 2, …}. In this case Xt is a discrete time stochastic process. • We use uppercase letter {Xt } to describe the process. A time series, {xt } is a realization or sample function from a certain process. • We use information from a time series to estimate parameters and properties of process {Xt }. week 2 2 Probability Distribution of a Process • For any stochastic process with index set I, its probability distribution function is uniquely determined by its finite dimensional distributions. •The k dimensional distribution function of a process is defined by FXX x,..., ( 1 x,..., k ) = P( Xt ≤ 1 ,..., xt≤ X k) x t1 tk 1 k for anyt 1 ,..., t k ∈ I and any real numbers x1, …, xk . -
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). -
Covariances of ARMA Models
Statistics 910, #9 1 Covariances of ARMA Processes Overview 1. Review ARMA models: causality and invertibility 2. AR covariance functions 3. MA and ARMA covariance functions 4. Partial autocorrelation function 5. Discussion Review of ARMA processes ARMA process A stationary solution fXtg (or if its mean is not zero, fXt − µg) of the linear difference equation Xt − φ1Xt−1 − · · · − φpXt−p = wt + θ1wt−1 + ··· + θqwt−q φ(B)Xt = θ(B)wt (1) 2 where wt denotes white noise, wt ∼ WN(0; σ ). Definition 3.5 adds the • identifiability condition that the polynomials φ(z) and θ(z) have no zeros in common and the • normalization condition that φ(0) = θ(0) = 1. Causal process A stationary process fXtg is said to be causal if there ex- ists a summable sequence (some require square summable (`2), others want more and require absolute summability (`1)) sequence f jg such that fXtg has the one-sided moving average representation 1 X Xt = jwt−j = (B)wt : (2) j=0 Proposition 3.1 states that a stationary ARMA process fXtg is causal if and only if (iff) the zeros of the autoregressive polynomial Statistics 910, #9 2 φ(z) lie outside the unit circle (i.e., φ(z) 6= 0 for jzj ≤ 1). Since φ(0) = 1, φ(z) > 0 for jzj ≤ 1. (The unit circle in the complex plane consists of those x 2 C for which jzj = 1; the closed unit disc includes the interior of the unit circle as well as the circle; the open unit disc consists of jzj < 1.) If the zeros of φ(z) lie outside the unit circle, then we can invert each Qp of the factors (1 − B=zj) that make up φ(B) = j=1(1 − B=zj) one at a time (as when back-substituting in the derivation of the AR(1) representation). -
Covariance Functions
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 Chapter 4 Covariance Functions We have seen that a covariance function is the crucial ingredient in a Gaussian process predictor, as it encodes our assumptions about the function which we wish to learn. From a slightly different viewpoint it is clear that in supervised learning the notion of similarity between data points is crucial; it is a basic similarity assumption that points with inputs x which are close are likely to have similar target values y, and thus training points that are near to a test point should be informative about the prediction at that point. Under the Gaussian process view it is the covariance function that defines nearness or similarity. An arbitrary function of input pairs x and x0 will not, in general, be a valid valid covariance covariance function.1 The purpose of this chapter is to give examples of some functions commonly-used covariance functions and to examine their properties. Section 4.1 defines a number of basic terms relating to covariance functions. Section 4.2 gives examples of stationary, dot-product, and other non-stationary covariance functions, and also gives some ways to make new ones from old. Section 4.3 introduces the important topic of eigenfunction analysis of covariance functions, and states Mercer’s theorem which allows us to express the covariance function (under certain conditions) in terms of its eigenfunctions and eigenvalues. The covariance functions given in section 4.2 are valid when the input domain X is a subset of RD. -
Lecture 1: Stationary Time Series∗
Lecture 1: Stationary Time Series∗ 1 Introduction If a random variable X is indexed to time, usually denoted by t, the observations {Xt, t ∈ T} is called a time series, where T is a time index set (for example, T = Z, the integer set). Time series data are very common in empirical economic studies. Figure 1 plots some frequently used variables. The upper left figure plots the quarterly GDP from 1947 to 2001; the upper right figure plots the the residuals after linear-detrending the logarithm of GDP; the lower left figure plots the monthly S&P 500 index data from 1990 to 2001; and the lower right figure plots the log difference of the monthly S&P. As you could see, these four series display quite different patterns over time. Investigating and modeling these different patterns is an important part of this course. In this course, you will find that many of the techniques (estimation methods, inference proce- dures, etc) you have learned in your general econometrics course are still applicable in time series analysis. However, there are something special of time series data compared to cross sectional data. For example, when working with cross-sectional data, it usually makes sense to assume that the observations are independent from each other, however, time series data are very likely to display some degree of dependence over time. More importantly, for time series data, we could observe only one history of the realizations of this variable. For example, suppose you obtain a series of US weekly stock index data for the last 50 years. -
Lecture 5: Gaussian Processes & Stationary Processes
Miranda Holmes-Cerfon Applied Stochastic Analysis, Spring 2019 Lecture 5: Gaussian processes & Stationary processes Readings Recommended: • Pavliotis (2014), sections 1.1, 1.2 • Grimmett and Stirzaker (2001), 8.2, 8.6 • Grimmett and Stirzaker (2001) 9.1, 9.3, 9.5, 9.6 (more advanced than what we will cover in class) Optional: • Grimmett and Stirzaker (2001) 4.9 (review of multivariate Gaussian random variables) • Grimmett and Stirzaker (2001) 9.4 • Chorin and Hald (2009) Chapter 6; a gentle introduction to the spectral decomposition for continuous processes, that gives insight into the link to the spectral decomposition for discrete processes. • Yaglom (1962), Ch. 1, 2; a nice short book with many details about stationary random functions; one of the original manuscripts on the topic. • Lindgren (2013) is a in-depth but accessible book; with more details and a more modern style than Yaglom (1962). We want to be able to describe more stochastic processes, which are not necessarily Markov process. In this lecture we will look at two classes of stochastic processes that are tractable to use as models and to simulat: Gaussian processes, and stationary processes. 5.1 Setup Here are some ideas we will need for what follows. 5.1.1 Finite dimensional distributions Definition. The finite-dimensional distributions (fdds) of a stochastic process Xt is the set of measures Pt1;t2;:::;tk given by Pt1;t2;:::;tk (F1 × F2 × ··· × Fk) = P(Xt1 2 F1;Xt2 2 F2;:::Xtk 2 Fk); k where (t1;:::;tk) is any point in T , k is any integer, and Fi are measurable events in R. -
STA 6857---Autocorrelation and Cross-Correlation & Stationary Time Series
Announcements Autocorrelation and Cross-Correlation Stationary Time Series Homework 1c STA 6857—Autocorrelation and Cross-Correlation & Outline Stationary Time Series (§1.4, 1.5) 1 Announcements 2 Autocorrelation and Cross-Correlation 3 Stationary Time Series 4 Homework 1c Arthur Berg STA 6857—Autocorrelation and Cross-Correlation & Stationary Time Series (§1.4, 1.5) 2/ 25 Announcements Autocorrelation and Cross-Correlation Stationary Time Series Homework 1c Announcements Autocorrelation and Cross-Correlation Stationary Time Series Homework 1c Homework Questions from Last Time We’ve seen the AR(p) and MA(q) models. Which one do we use? In scientific modeling we wish to choose the model that best Our TA, Aixin Tan, will have office hours on Thursdays from 1–2pm describes or explains the data. Later we will develop many in 218 Griffin-Floyd. techniques to help us choose and fit the best model for our data. Homework 1c will be assigned today and the last part of homework Is this white noise process in the models unique? 1, homework 1d, will be assigned on Friday. Homework 1 will be We will see later that any stationary time series can be described as collected on Friday, September 7. Don’t wait till the last minute to do a MA(1) + “deterministic part” by the Wold decomposition, where the homework, because more homework will follow next week. the white noise process in the MA(1) part is unique. So in short, the answer is yes. We will also see later how to estimate the white noise process which will aid in forecasting. -
Introduction to Random Fields and Scale Invariance Hermine Biermé
Introduction to random fields and scale invariance Hermine Biermé To cite this version: Hermine Biermé. Introduction to random fields and scale invariance. 2017. hal-01493834v2 HAL Id: hal-01493834 https://hal.archives-ouvertes.fr/hal-01493834v2 Preprint submitted on 5 May 2018 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. Introduction to random fields and scale invariance Hermine Bierme´ Abstract In medical imaging, several authors have proposed to characterize rough- ness of observed textures by their fractal dimensions. Fractal analysis of 1D signals is mainly based on the stochastic modeling using the famous fractional Brownian motion for which the fractal dimension is determined by its so-called Hurst param- eter. Lots of 2D generalizations of this toy model may be defined according to the scope. This lecture intends to present some of them. After an introduction to random fields, the first part will focus on the construction of Gaussian random fields with prescribed invariance properties such as stationarity, self-similarity, or operator scal- ing property. Sample paths properties such as modulus of continuity and Hausdorff dimension of graphs will be settled in the second part to understand links with frac- tal analysis. -
Basic Properties of the Multivariate Fractional Brownian Motion Pierre-Olivier Amblard, Jean-François Coeurjolly, Frédéric Lavancier, Anne Philippe
Basic properties of the Multivariate Fractional Brownian Motion Pierre-Olivier Amblard, Jean-François Coeurjolly, Frédéric Lavancier, Anne Philippe To cite this version: Pierre-Olivier Amblard, Jean-François Coeurjolly, Frédéric Lavancier, Anne Philippe. Basic properties of the Multivariate Fractional Brownian Motion. Séminaires et congrès, Société mathématique de France, 2013, 28, pp.65-87. hal-00497639v2 HAL Id: hal-00497639 https://hal.archives-ouvertes.fr/hal-00497639v2 Submitted on 25 Apr 2012 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. BASIC PROPERTIES OF THE MULTIVARIATE FRACTIONAL BROWNIAN MOTION PIERRE-OLIVIER AMBLARD, JEAN-FRANÇOIS COEURJOLLY, FRÉDÉRIC LAVANCIER, AND ANNE PHILIPPE Abstract. This paper reviews and extends some recent results on the multivariate frac- tional Brownian motion (mfBm) and its increment process. A characterization of the mfBm through its covariance function is obtained. Similarly, the correlation and spectral analyses of the increments are investigated. On the other hand we show that (almost) all mfBm’s may be reached as the limit of partial sums of (super)linear processes. Fi- nally, an algorithm to perfectly simulate the mfBm is presented and illustrated by some simulations. 1. Introduction The fractional Brownian motion is the unique Gaussian self-similar process with station- ary increments. -
Statistics 153 (Time Series) : Lecture Five
Statistics 153 (Time Series) : Lecture Five Aditya Guntuboyina 31 January 2012 1 Contents 1. Definitions of strong and weak stationary sequences. 2. Examples of stationary sequences. 2 Stationarity The concept of a stationary time series is the most important thing in this course. Stationary time series are typically used for the residuals after trend and seasonality have been removed. Stationarity allows a systematic study of time series forecasting. In order to avoid confusion, we shall, from now on, make a distinction be- tween the observed time series data x1; : : : ; xn and the sequence of random vari- ables X1;:::;Xn. The observed data x1; : : : ; xn is a realization of X1;:::;Xn. It is convenient of think of the random variables fXtg as forming a doubly infinite sequence: :::;X−2;X−1;X0;X1;X2;:::: The notion of stationarity will apply to the doubly infinite sequence of ran- dom variables fXtg. Strictly speaking, stationarity does not apply to the data x1; : : : ; xn. However, one frequently abuses terminology and uses stationary data to mean that the random variables of which the observed data are a real- ization have a stationary distribution. 1 Definition 2.1 (Strict or Strong Stationarity). A doubly infinite se- quence of random variables fXtg is strictly stationary if the joint dis- tribution of (Xt1 ;Xt2 ;:::;Xtk ) is the same as the joint distribution of (Xt1+h;Xt2+h;:::;Xtk+h) for every choice of times t1; : : : ; tk and h. Roughly speaking, stationarity means that the joint distribution of the ran- dom variables remains constant over time. For example, under stationarity, the joint distribution of today's and tomorrow's random variables is the same as the joint distribution of the variables from any two successive days (past or future). -
Mixed Effects Models for Complex Data
Mixed Effects Models for Complex Data Lang Wu . University of British Columbia Contents 1 Introduction 3 1.1 Introduction 3 1.2 Longitudinal Data and Clustered Data 5 1.3 Some Examples 8 1.3.1 A Study on Mental Distress 8 1.3.2 An AIDS Study 11 1.3.3 A Study on Students’ Performance 14 1.3.4 A Study on Children’s Growth 15 1.4 Regression Models 16 1.4.1 General Concepts and Approaches 16 1.4.2 Regression Models for Cross-Sectional Data 18 1.4.3 Regression Models for Longitudinal Data 22 1.4.4 Regression Models for Survival Data 25 1.5 Mixed Effects Models 26 1.5.1 Motivating Examples 26 1.5.2 LME Models 29 1.5.3 GLMM, NLME, and Frailty Models 31 1.6 Complex or Incomplete Data 32 1.6.1 Missing Data 33 1.6.2 Censoring, Measurement Error, and Outliers 34 1.6.3 Simple Methods 35 1.7 Software 36 1.8 Outline and Notation 37 iii iv MIXED EFFECTS MODELS FOR COMPLEX DATA 2 Mixed Effects Models 41 2.1 Introduction 41 2.2 Linear Mixed Effects (LME) Models 43 2.2.1 Linear Regression Models 43 2.2.2 LME Models 44 2.3 Nonlinear Mixed Effects (NLME) Models 51 2.3.1 Nonlinear Regression Models 51 2.3.2 NLME Models 54 2.4 Generalized Linear Mixed Models (GLMMs) 60 2.4.1 Generalized Linear Models (GLMs) 60 2.4.2 GLMMs 64 2.5 Nonparametric and Semiparametric Mixed Effects Models 68 2.5.1 Nonparametric and Semiparametric Regression Models 68 2.5.2 Nonparametric and Semiparametric Mixed Effects Models 76 2.6 Computational Strategies 80 2.6.1 “Exact” Methods 83 2.6.2 EM Algorithms 85 2.6.3 Approximate Methods 87 2.7 Further Topics 91 2.7.1 Model Selection and