APPLYING BUCKET RANDOM PERMUTATIONS TO STATIONARY SEQUENCES WITH LONG-RANGE DEPENDENCE

Fractals ◽  
2007 ◽  
Vol 15 (02) ◽  
pp. 105-126 ◽  
Author(s):  
YINGCHUN ZHOU ◽  
MURAD S. TAQQU
Keyword(s):  
Long Range ◽  
Short Range ◽  
Random Permutation ◽  
Stationary Sequence ◽  
Long Range Dependence ◽  
Finite Variance ◽  
Dependence Structure ◽  
Random Permutations ◽  
Medium Range ◽  
The Time Domain

Bucket random permutations (shuffling) are used to modify the dependence structure of a time series, and this may destroy long-range dependence, when it is present. Three types of bucket permutations are considered here: external, internal and two-level permutations. It is commonly believed that (1) an external random permutation destroys the long-range dependence and keeps the short-range dependence, (2) an internal permutation destroys the short-range dependence and keeps the long-range dependence, and (3) a two-level permutation distorts the medium-range dependence while keeping both the long-range and short-range dependence. This paper provides a theoretical basis for investigating these claims. It extends the study started in Ref. 1 and analyze the effects that these random permutations have on a long-range dependent finite variance stationary sequence both in the time domain and in the frequency domain.

Parametric modelling of turbulence

1990 ◽  
Vol 332 (1627) ◽  
pp. 439-455 ◽  
Keyword(s):  
Long Range ◽  
Three Dimensional ◽  
Large Data ◽  
Theoretical Work ◽  
Long Range Dependence ◽  
Dependence Structure ◽  
Data Sets ◽  
Parametric Modelling ◽  
First Order ◽  
Log Linear

Some steps are taken towards a parametric statistical model for the velocity and velocity derivative fields in stationary turbulence, building on the background of existing theoretical and empirical knowledge of such fields. While the ultimate goal is a model for the three-dimensional velocity components, and hence for the corresponding velocity derivatives, we concentrate here on the stream wise velocity component. Discrete and continuous time stochastic processes of the first-order autoregressive type and with one-dimensional marginals having log-linear tails are constructed and compared with two large data-sets. It turns out that a first-order autoregression that fits the local correlation structure well is not capable of describing the correlations over longer ranges. A good fit locally as well as at longer ranges is achieved by using a process that is the sum of two independent autoregressions. We study this type of model in some detail. We also consider a model derived from the above-mentioned autoregressions and with dependence structure on the borderline to long-range dependence. This model is obtained by means of a general method for construction of processes with long-range dependence. Some suggestions for future empirical and theoretical work are given.

Correlation models with long-range dependence

2002 ◽  
Vol 39 (2) ◽  
pp. 370-382 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Time Series ◽  
Long Range ◽  
Long Memory ◽  
Short Range ◽  
Sufficient Conditions ◽  
Long Range Dependence ◽  
Simple Method ◽  
Summable Sequence ◽  
Correlation Models ◽  
Necessary And Sufficient

This paper is concerned with the correlation structure of a stationary discrete time-series with long memory or long-range dependence. Given a sequence of bounded variation, we obtain necessary and sufficient conditions for a function generated from the sequence to be a proper correlation function. These conditions are applied to derive various slowly decaying correlation models. To obtain correlation models with short-range dependence from an absolutely summable sequence, a simple method is introduced.

Power-law correlations and other models with long-range dependence on a lattice

2003 ◽  
Vol 40 (3) ◽  
pp. 690-703 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Long Range ◽  
Power Law ◽  
Correlation Functions ◽  
Short Range ◽  
Long Range Dependence ◽  
Correlation Model ◽  
Double Sequences ◽  
Exact Power ◽  
Stationary Random Field ◽  
Dependent Correlation

This paper introduces long-range dependence for a stationary random field on a plane lattice, derives an exact power-law correlation model and other models with long-range dependence on the lattice, and explores the close connection between short-range dependent correlation functions and absolutely summable double sequences.

Some simple properties of sums of random variables having long-range dependence

1989 ◽  
Vol 424 (1867) ◽  
pp. 255-262 ◽  
Keyword(s):  
Numerical Methods ◽  
Long Range ◽  
Random Variables ◽  
Higher Order ◽  
Long Range Dependence ◽  
Finite Variance ◽  
Sums Of Random Variables ◽  
Tensor Methods ◽  
Non Gaussian ◽  
Special Case

The higher-order moments and cumulants of sums of a special case of random variables having long-range dependence are investigated. Tensor methods are used to simplify the calculations. The limiting form of the second and third cumulants as the number of variables added becomes large is studied by analytical and numerical methods. The implications are discussed for the existence of non-gaussian limits of sums of random quantities of finite variance and long-range dependence.

Power-law correlations and other models with long-range dependence on a lattice

2003 ◽  
Vol 40 (03) ◽  
pp. 690-703 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Long Range ◽  
Power Law ◽  
Correlation Functions ◽  
Short Range ◽  
Long Range Dependence ◽  
Correlation Model ◽  
Double Sequences ◽  
Exact Power ◽  
Stationary Random Field ◽  
Dependent Correlation

This paper introduces long-range dependence for a stationary random field on a plane lattice, derives an exact power-law correlation model and other models with long-range dependence on the lattice, and explores the close connection between short-range dependent correlation functions and absolutely summable double sequences.

scholarly journals On the long-range dependence of fractional Poisson and negative binomial processes

2016 ◽  
Vol 53 (4) ◽  
pp. 989-1000 ◽  
Author(s):  
A. Maheshwari ◽  
P. Vellaisamy
Keyword(s):  
Poisson Process ◽  
Long Range ◽  
Short Range ◽  
Negative Binomial ◽  
Stationary Process ◽  
Long Range Dependence ◽  
Fractional Poisson Process ◽  
Poissonian Noise ◽  
Negative Binomial Process ◽  
Binomial Process

Abstract We discuss the short-range dependence (SRD) property of the increments of the fractional Poisson process, called the fractional Poissonian noise. We also establish that the fractional negative binomial process (FNBP) has the long-range dependence (LRD) property, while the increments of the FNBP have the SRD property. Our definitions of the SRD/LRD properties are similar to those for a stationary process and different from those recently used in Biard and Saussereau (2014).

Correlation models with long-range dependence

2002 ◽  
Vol 39 (02) ◽  
pp. 370-382 ◽  
Author(s):  
Chunsheng Ma
Keyword(s):  
Time Series ◽  
Long Range ◽  
Long Memory ◽  
Short Range ◽  
Sufficient Conditions ◽  
Long Range Dependence ◽  
Simple Method ◽  
Summable Sequence ◽  
Correlation Models ◽  
Necessary And Sufficient

This paper is concerned with the correlation structure of a stationary discrete time-series with long memory or long-range dependence. Given a sequence of bounded variation, we obtain necessary and sufficient conditions for a function generated from the sequence to be a proper correlation function. These conditions are applied to derive various slowly decaying correlation models. To obtain correlation models with short-range dependence from an absolutely summable sequence, a simple method is introduced.

scholarly journals Student processes

2005 ◽  
Vol 37 (02) ◽  
pp. 342-365 ◽  
Author(s):  
C. C. Heyde ◽  
N. N. Leonenko
Keyword(s):  
Time Series ◽  
Stochastic Processes ◽  
Long Range ◽  
Risky Asset ◽  
Long Range Dependence ◽  
Dependence Structure

Stochastic processes with Student marginals and various types of dependence structure, allowing for both short- and long-range dependence, are discussed in this paper. A particular motivation is the modelling of risky asset time series.

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