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Mixing properties of ARMA processes
Stochastic Processes and Their Applications, 1988 Let \(\{\) Y(t)\(\}\) be a stationary process in R l. Denote by \({\mathcal A}_ 0\) and \({\mathcal A}_ k\) the \(\sigma\)-algebra generated by \(\{\) Y(t),t\(\leq 0\}\) and \(\{\) Y(t),t\(\geq k\}\), respectively. Define \[ \beta (k)=E\sup_{B\in {\mathcal A}\quad k}| P(B| {\mathcal A}_ 0)- P(B)|. \] If there exists \(\rho\in (0,1)\) such that \(\beta (
Mokkadem, Abdelkader +1 more
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Population Genomics Provides Insights Into Genomic Features of Inbreeding Depression in Arma Chinensis [PDF]
Evolutionary ApplicationsArma chinensis, a predatory insect renowned for its prey diversity in East Asia, is effective in controlling agricultural and forestry pests. However, after introducing field populations into indoor subcultures, features of inbreeding depression have ...
Bin Li +5 more
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Time series with infinite-order partial copula dependence
Dependence Modeling, 2022 Stationary and ergodic time series can be constructed using an s-vine decomposition based on sets of bivariate copula functions. The extension of such processes to infinite copula sequences is considered and shown to yield a rich class of models that ...
Bladt Martin, McNeil Alexander J.
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On the Adequacy of Using T Approximation in Bayesian Inferences of Arma Models [PDF]
The Egyptian Statistical Journal, 1999 The main objective of this paper is to examine the adequacy of using the t approximation in handling the Bayesian inferential problems of autoregressive-moving average (ARMA) processes.
Samir Shaarawy, Gamal El-Shawadfy
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ARMA–GARCH model with fractional generalized hyperbolic innovations
Financial Innovation, 2022 In this study, a multivariate ARMA–GARCH model with fractional generalized hyperbolic innovations exhibiting fat-tail, volatility clustering, and long-range dependence properties is introduced. To define the fractional generalized hyperbolic process, the
Sung Ik Kim
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Modelling Volatile Time Series with V-Transforms and Copulas
Risks, 2021 An approach to the modelling of volatile time series using a class of uniformity-preserving transforms for uniform random variables is proposed. V-transforms describe the relationship between quantiles of the stationary distribution of the time series ...
Alexander J. McNeil
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On a Class of Z+-Valued Autoregressive Moving Average (ARMA) Processes
Revstat Statistical Journal, 2008 A convolution semigroup of probability generating functions and its related operator ⊙F are used to construct a class of stationary Z+-valued autoregressive moving average (ARMA) processes.
Emad-Eldin A. A. Aly , Nadjib Bouzar
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Root tracking using time-varying autoregressive moving average models and sigma-point Kalman filters
EURASIP Journal on Advances in Signal Processing, 2020 Root tracking is a powerful technique that provides insight into the mechanisms of various time-varying processes. The poles and the zeros of a signal-generating system determine the spectral characteristics of the signal under consideration.
Kyriaki Kostoglou, Michael Lunglmayr
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Functional clustering of periodic transcriptional profiles through ARMA(p,q). [PDF]
PLoS ONE, 2010 BackgroundGene clustering of periodic transcriptional profiles provides an opportunity to shed light on a variety of biological processes, but this technique relies critically upon the robust modeling of longitudinal covariance structure over time ...
Ning Li +5 more
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Bayesian Identification of Seasonal Vector ARMA Processes [PDF]
The Egyptian Statistical JournalThis research paper uses the Bayesian approach to establish an approximate method to specify the four orders of multivariate seasonal autoregressive moving average (SARMA) models. The proposed methodology consists of four coherent consecutive steps.
Samir M. Shaarawy +2 more
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