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Relevant bibliographies by topics / Multivariate stationary process

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Journal articles

Academic literature on the topic 'Multivariate stationary process'

Author: Grafiati

Published: 9 November 2024

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Journal articles on the topic "Multivariate stationary process"

1

MBEKE, Kévin Stanislas, and Ouagnina Hili. "Estimation of a stationary multivariate ARFIMA process." Afrika Statistika 13, no. 3 (2018): 1717–32. http://dx.doi.org/10.16929/as/1717.130.

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2

Cheng, R., and M. Pourahmadi. "The mixing rate of a stationary multivariate process." Journal of Theoretical Probability 6, no. 3 (1993): 603–17. http://dx.doi.org/10.1007/bf01066720.

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3

Latour, Alain. "The Multivariate Ginar(p) Process." Advances in Applied Probability 29, no. 1 (1997): 228–48. http://dx.doi.org/10.2307/1427868.

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A criterion is given for the existence of a stationary and causal multivariate integer-valued autoregressive process, MGINAR(p). The autocovariance function of this process being identical to the autocovariance function of a standard Gaussian MAR(p), we deduce that the MGINAR(p) process is nothing but a MAR(p) process. Consequently, the spectral density is directly found and gives good insight into the stochastic structure of a MGINAR(p). The estimation of parameters of the model, as well as the forecasting of the series, is discussed.

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4

Latour, Alain. "The Multivariate Ginar(p) Process." Advances in Applied Probability 29, no. 01 (1997): 228–48. http://dx.doi.org/10.1017/s0001867800027865.

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A criterion is given for the existence of a stationary and causal multivariate integer-valued autoregressive process, MGINAR(p). The autocovariance function of this process being identical to the autocovariance function of a standard Gaussian MAR(p), we deduce that the MGINAR(p) process is nothing but a MAR(p) process. Consequently, the spectral density is directly found and gives good insight into the stochastic structure of a MGINAR(p). The estimation of parameters of the model, as well as the forecasting of the series, is discussed.

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5

Sun, Ying, Ning Su, and Yue Wu. "Multivariate stationary non-Gaussian process simulation for wind pressure fields." Earthquake Engineering and Engineering Vibration 15, no. 4 (2016): 729–42. http://dx.doi.org/10.1007/s11803-016-0361-x.

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6

Borovkov, K., and G. Last. "On Rice's Formula for Stationary Multivariate Piecewise Smooth Processes." Journal of Applied Probability 49, no. 02 (2012): 351–63. http://dx.doi.org/10.1017/s002190020000913x.

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LetX= {Xt:t≥ 0} be a stationary piecewise continuousRd-valued process that moves between jumps along the integral curves of a given continuous vector field, and letS⊂Rdbe a smooth surface. The aim of this paper is to derive a multivariate version of Rice's formula, relating the intensity of the point process of (localized) continuous crossings ofSbyXto the distribution ofX0. Our result is illustrated by examples relating to queueing networks and stress release network models.

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7

Zhang, Zhengjun, and Richard L. Smith. "The behavior of multivariate maxima of moving maxima processes." Journal of Applied Probability 41, no. 4 (2004): 1113–23. http://dx.doi.org/10.1239/jap/1101840556.

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In the characterization of multivariate extremal indices of multivariate stationary processes, multivariate maxima of moving maxima processes, or M4 processes for short, have been introduced by Smith and Weissman. Central to the introduction of M4 processes is that the extreme observations of multivariate stationary processes may be characterized in terms of a limiting max-stable process under quite general conditions, and that a max-stable process can be arbitrarily closely approximated by an M4 process. In this paper, we derive some additional basic probabilistic properties for a finite clas

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8

Zhang, Zhengjun, and Richard L. Smith. "The behavior of multivariate maxima of moving maxima processes." Journal of Applied Probability 41, no. 04 (2004): 1113–23. http://dx.doi.org/10.1017/s0021900200020878.

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Abstract:

In the characterization of multivariate extremal indices of multivariate stationary processes, multivariate maxima of moving maxima processes, or M4 processes for short, have been introduced by Smith and Weissman. Central to the introduction of M4 processes is that the extreme observations of multivariate stationary processes may be characterized in terms of a limiting max-stable process under quite general conditions, and that a max-stable process can be arbitrarily closely approximated by an M4 process. In this paper, we derive some additional basic probabilistic properties for a finite clas

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9

Borovkov, K., and G. Last. "On Rice's Formula for Stationary Multivariate Piecewise Smooth Processes." Journal of Applied Probability 49, no. 2 (2012): 351–63. http://dx.doi.org/10.1239/jap/1339878791.

Full text

Abstract:

Let X = {Xt: t ≥ 0} be a stationary piecewise continuous Rd-valued process that moves between jumps along the integral curves of a given continuous vector field, and let S ⊂ Rd be a smooth surface. The aim of this paper is to derive a multivariate version of Rice's formula, relating the intensity of the point process of (localized) continuous crossings of S by X to the distribution of X0. Our result is illustrated by examples relating to queueing networks and stress release network models.

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10

Gordy, Michael B. "Finite-Dimensional Distributions of a Square-Root Diffusion." Journal of Applied Probability 51, no. 4 (2014): 930–42. http://dx.doi.org/10.1239/jap/1421763319.

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We derive multivariate moment generating functions for the conditional and stationary distributions of a discrete sample path of n observations of a square-root diffusion (CIR) process, X(t). For any fixed vector of observation times t1,…,tn, we find the conditional joint distribution of (X(t1),…,X(tn)) is a multivariate noncentral chi-squared distribution and the stationary joint distribution is a Krishnamoorthy-Parthasarathy multivariate gamma distribution. Multivariate cumulants of the stationary distribution have a simple and computationally tractable expression. We also obtain the moment

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