Results 161 to 170 of about 4,545 (215)

ARMA process for speckled data

Journal of Statistical Computation and Simulation, 2021
Synthetic aperture radar (SAR) systems are efficient to deal with remote sensing issues. In contrast, SAR images are affected by speckle noise, due to the use of coherent illumination in their capt...
Pedro M. Almeida-Junior, Abraão D. C. Nascimento   +1 more
openaire   +1 more source

Discrete-valued ARMA processes

Statistics & Probability Letters, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Biswas, Atanu, Song, Peter X.-K.
openaire   +2 more sources

ARMA processes: order estimation

ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing, 2003
The authors study, for an ARMA (autoregressive moving-average) (p/sub 0/, q/sub 0/) process, the joint determination from a finite data sample of its structural parameters p/sub 0/ and q/sub 0/, its AR and MA components, and its innovation power sigma /sup 2/.
M.I. Ribeiro, J.M.F. Moura
openaire   +1 more source

Simple correlated arma processes

Series Statistics, 1984
Let \(\{x_ t\}\) and \(\{y_ t\}\) be ARMA (autoregressive-moving average) processes, defined in terms of operators acting respectively on processes \(a_ t\) and \(b_ t\) such that \((a_ t,b_ t)\) is bivariate normal with means 0, variances \(\sigma^ 2_ a\) and \(\sigma^ 2_ b\), and correlation \(\rho\). Then the bivariate process \(\{(x_ t,y_ t)\}\) is
openaire   +2 more sources

Stationary ARMA Processes

1987
In this chapter we introduce an extremely important class of time series {X t , t = 0, ± 1, ± 2,...} defined in terms of linear difference equations with constant coefficients. The imposition of this additional structure defines a parametric family of stationary processes, the autoregressive moving average or ARMA processes.
Peter J. Brockwell, Richard A. Davis
openaire   +1 more source

On shifted multiple arma processes

Series Statistics, 1981
Let {X t} be a p-dimensional ARMA (m n) process. Write where have q and r components, respecctivelyq+r=p). Put . It is proved that {Y t} is an ARMA (m -1. n + 1) process and a procedure for evaluation of its matrices of coefficients is given. If {X t} is an AR (m) process, then {Y t is an AR (m -f1) process; if {X,tis an MA(n-) process, then {Y t} is ...
openaire   +2 more sources

Model Identification of Integrated ARMA Processes

Multivariate Behavioral Research, 2008
This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes.
Tetiana, Stadnytska, Simone, Braun, Joachim, Werner   +2 more
openaire   +2 more sources

Spectral estimation of ARMA processes using ARMA-cepstrum recursion

IEEE Signal Processing Letters, 2000
In this letter, the spectral estimation problem of a stationary autoregressive moving average (ARMA) process is considered, and a new method for the estimation of the MA part is proposed. A simple recursion relating the ARMA parameters and the cepstral coefficients of an ARMA process is derived and utilized for the estimation of the MA parameters.
A. Kaderli, A.S. Kayhan
openaire   +1 more source

Continuous-time fractional ARMA processes

Statistics & Probability Letters, 1994
Continuous-time fractional ARMA processes \((X_ t)\), \(t \geq 0\), are introduced by \[ X_ t = \int^ t _{-\infty} f(t - s) dW(s). \] Here, \((W(t))\) is a Brownian motion. The impulse response function \(f \in L^ 2(R^ +)\) has a Laplace transform \(F\) of the form \[ F(s) = \prod^ K_ 1 (s - a_ k)^{d_ k} \text{ if Re}(s) > a \] for certain parameters \(
Viano, M. C., Deniau, C., Oppenheim, G.
openaire   +1 more source

Modelling and Forecasting with ARMA Processes

1996
The determination of an appropriate ARMA(p, q) model to represent an observed stationary time series involves a number of interrelated problems. These include the choice of p and q (order selection) and estimation of the mean, the coefficients {ϕ i , i = 1, …, p}, {θ i , i = 1, …, q}, and the white noise variance σ2.
Peter J. Brockwell, Richard A. Davis
openaire   +1 more source