A new test for ARMA models with errors following a general white noise process

Test ◽  
1996 ◽  
Vol 5 (1) ◽  
pp. 187-202 ◽  
Author(s):  
E. Gonçalves ◽  
P. Jacob ◽  
N. Mendes Lopes
Keyword(s):  
White Noise ◽  
Arma Models ◽  
White Noise Process ◽  
Noise Process

scholarly journals Empirical White Noise Processes and the Subjective Probabilistic Approaches

2019 ◽  
Vol 48 (1) ◽  
pp. 19-30
Author(s):  
András Rövid ◽  
László Palkovics ◽  
Péter Várlaki
Keyword(s):  
White Noise ◽  
Random Number ◽  
White Noise Process ◽  
Random Number Generators ◽  
Noise Process ◽  
Complementary Approach ◽  
Fuzzy Random

The paper discusses the identification of the empirical white noise processes generated by deterministic numerical algorithms.The introduced fuzzy-random complementary approach can identify the inner hidden correlational patterns of the empirical white noise process if the process has a real hidden structure of this kind. We have shown how the characteristics of auto-correlated white noise processes change as the order of autocorrelation increases. Although in this paper we rely on random number generators to get approximate white noise processes, in our upcoming research we are planning to turn the focus on physical white noise processes in order to validate our hypothesis.

The exact bispectra for bilinear realizable processes with hermite degree 2

1991 ◽  
Vol 23 (04) ◽  
pp. 798-808 ◽  
Author(s):  
György Terdik ◽  
Laurie Meaux
Keyword(s):  
White Noise ◽  
Discrete Time ◽  
Hermite Polynomial ◽  
Transfer Functions ◽  
Gaussian White Noise ◽  
Second Order ◽  
Bilinear Model ◽  
White Noise Process ◽  
Noise Process ◽  
Gaussian White Noise Process

This paper deals with the stationary bilinear model with Hermite degree 2 in discrete time which is built up by the first- and second-order Hermite polynomial of a Gaussian white noise process. The exact spectrum and bispectrum is constructed in terms of the transfer functions of the model.

Statistics of an Appealing Class of Random Processes

2021 ◽  
pp. 260-276
Author(s):  
Shaival Hemant Nagarsheth ◽  
Shambhu Nath Sharma
Keyword(s):  
White Noise ◽  
Stochastic Systems ◽  
Correction Term ◽  
Random Processes ◽  
Random Perturbations ◽  
White Noise Process ◽  
Coloured Noise ◽  
Diffusion Operator ◽  
Noise Process ◽  
Special Cases

The white noise process, the Ornstein-Uhlenbeck process, and coloured noise process are salient noise processes to model the effect of random perturbations. In this chapter, the statistical properties, the master's equations for the Brownian noise process, coloured noise process, and the OU process are summarized. The results associated with the white noise process would be derived as the special cases of the Brownian and the OU noise processes. This chapter also formalizes stochastic differential rules for the Brownian motion and the OU process-driven vector stochastic differential systems in detail. Moreover, the master equations, especially for the coloured noise-driven stochastic differential system as well as the OU noise process-driven, are recast in the operator form involving the drift and modified diffusion operators involving an additional correction term to the standard diffusion operator. The results summarized in this chapter will be useful for modelling a random walk in stochastic systems.

The exact bispectra for bilinear realizable processes with hermite degree 2

1991 ◽  
Vol 23 (4) ◽  
pp. 798-808 ◽  
Author(s):  
György Terdik ◽  
Laurie Meaux
Keyword(s):  
White Noise ◽  
Discrete Time ◽  
Hermite Polynomial ◽  
Transfer Functions ◽  
Gaussian White Noise ◽  
Second Order ◽  
Bilinear Model ◽  
White Noise Process ◽  
Noise Process ◽  
Gaussian White Noise Process

This paper deals with the stationary bilinear model with Hermite degree 2 in discrete time which is built up by the first- and second-order Hermite polynomial of a Gaussian white noise process. The exact spectrum and bispectrum is constructed in terms of the transfer functions of the model.

Optimal Wiener Filter for a Body Mounted Inertial Attitude Sensor

2008 ◽  
Vol 61 (3) ◽  
pp. 455-472 ◽  
Author(s):  
Peter Rizun
Keyword(s):  
Spectral Density ◽  
White Noise ◽  
Power Spectral Density ◽  
Human Body ◽  
Linear Acceleration ◽  
Mean Square ◽  
White Noise Process ◽  
Noise Process ◽  
Power Spectral ◽  
The Mean

An optimal attitude estimator is presented for a human body-mounted inertial measurement unit employing orthogonal triads of gyroscopes, accelerometers and magnetometers. The estimator continuously fuses gyroscope and accelerometer measurements together in a manner that minimizes the mean square error in the estimate of the gravity vector, based on known spectral characteristics for the gyroscope noise and the linear acceleration of points on the human body. The gyroscope noise is modelled as a white noise process of power spectral density δn2/2 while the linear acceleration is modelled as the derivative of a band-limited white noise process of power spectral density δv2/2. The estimator is robust to centripetal acceleration and guaranteed to have zero mean error regardless of the motion of the sensor. The mean square angular error in attitude is shown to be independent of the module's angular velocity and equal to 21/2g−1/2δn3/2δv1/2.

scholarly journals Phase Congruential White Noise Generator

Algorithms ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 118
Author(s):  
Aleksei Deon ◽  
Oleg Karaduta ◽  
Yulian Menyaev
Keyword(s):  
White Noise ◽  
Random Variables ◽  
Noise Generator ◽  
Signal Generator ◽  
White Noise Process ◽  
New Approach ◽  
Noise Process ◽  
White Noise Generator ◽  
Phase Signal ◽  
Simulation Results

White noise generators can use uniform random sequences as a basis. However, such a technology may lead to deficient results if the original sequences have insufficient uniformity or omissions of random variables. This article offers a new approach for creating a phase signal generator with an improved matrix of autocorrelation coefficients. As a result, the generated signals of the white noise process have absolutely uniform intensities at the eigen Fourier frequencies. The simulation results confirm that the received signals have an adequate approximation of uniform white noise.

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