Humbert generalized fractional differenced ARMA processes
Communications in Nonlinear Science and Numerical Simulation, 2023 In this article, we use the generating functions of the Humbert polynomials to define two types of Humbert generalized fractional differenced ARMA processes. We present stationarity and invertibility conditions for the introduced models. The singularities for the spectral densities of the introduced models are obtained.
Niharika Bhootna +3 more
openaire +3 more sources
Tensorial products of functional ARMA processes
Journal of Multivariate Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bosq, Denis
openaire +3 more sources
Estimating the Orders of Bivariate Mixed ARMA (p,q) Processes Using Bayesian Approach [PDF]
The Egyptian Statistical Journal, 2016 Estimating the orders of bivariate mixed autoregressive moving average processes, denoted by ARMA2 (p,q) , is the first and one of the most important phases in time series analysis. This article has three different objectives.
Emad E.A. Soliman
doaj +1 more source
On model fitting and estimation of strictly stationary processes
Modern Stochastics: Theory and Applications, 2017 Stationary processes have been extensively studied in the literature. Their applications include modeling and forecasting numerous real life phenomena such as natural disasters, sales and market movements.
Marko Voutilainen +2 more
doaj +1 more source
Indirect and Direct Bayesian Techniques to Identify the Orders of Vector ARMA Processes [PDF]
The Egyptian Statistical Journal, 2018 This article develops two bayesian techniques to identify the orders of vector mixed autogressive moving average processes namely the indirect and direct techniques. The proposed indirect technique approximates the joint posterior
Samir M. Shaarawy +2 more
doaj +1 more source
A Continuous Time GARCH Process of Higher Order [PDF]
, 2005 A continuous time GARCH model of order (p,q) is introduced, which is driven by a single Lévy process. It extends many of the features of discrete time GARCH(p,q) processes to a continuous time setting.
Lindner, Alexander M. +2 more
core +1 more source
An Effectiveness Study Of Bayesian Identification Techniques for ARMA Models [PDF]
The Egyptian Statistical Journal, 2009 Model identification is the first and most important stage when analyzing a time series. As a result of analytical complexity, very little has been done from a Bayesian viewpoint in order to identify the orders of ARMA models.
Sherif S. Ali
doaj +1 more source
Autoregressive Models with Time-Dependent Coefficients—A Comparison between Several Approaches
Stats, 2022 Autoregressive-moving average (ARMA) models with time-dependent (td) coefficients and marginally heteroscedastic innovations provide a natural alternative to stationary ARMA models. Several theories have been developed in the last 25 years for parametric
Rajae Azrak, Guy Mélard
doaj +1 more source
Measuring the Distance between Sets of ARMA Models
Econometrics, 2016 A distance between pairs of sets of autoregressive moving average (ARMA) processes is proposed. Its main properties are discussed. The paper also shows how the proposed distance finds application in time series analysis.
Umberto Triacca
doaj +1 more source
A Comparative Study on a Triple-Concept Model of Two Techniques for Monitoring the Mean of Stationary Processes [PDF]
International Journal of Industrial Engineering and Production Research, 2021 In recent years, it has been proven that integrating statistical process control, maintenance policy, and production can bring more benefits for the entire production systems.
Samrad Jafarian-Namin +4 more
doaj