Mixing properties of ARMA processes

DOI10.1016/0304-4149(88)90045-2zbMath0647.60042OpenAlexW1996484339WikidataQ127642338 ScholiaQ127642338MaRDI QIDQ1104632

Abdelkader Mokkadem

Publication date: 1988

Published in: Stochastic Processes and their Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0304-4149(88)90045-2

zbMATH Keywords

Markov chainsinnovationsstrong mixinggeometric ergodicityabsolutely continuous distributionGCRgeometrically completely regular processesstationary vector ARMA processes

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Stationary stochastic processes (60G10) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10)

Related Items

A dependent multiplier bootstrap for the sequential empirical copula process under strong mixing ⋮ Greedy algorithms for prediction ⋮ Asymptotic properties in partial linear models under dependence ⋮ A consistent bootstrap test for conditional density functions with time-series data ⋮ Wavelet density estimation for mixing and size-biased data ⋮ Testing the constancy of Spearman's rho in multivariate time series ⋮ The Berry–Esseen-type bound for the G-M estimator in a nonparametric regression model with α-mixing errors ⋮ A NONPARAMETRIC ESTIMATOR FOR THE COVARIANCE FUNCTION OF FUNCTIONAL DATA ⋮ \(M\)-type regression splines involving time series ⋮ R\'enyi entropy and pattern matching for run-length encoded sequences ⋮ MISE of wavelet estimators for regression derivatives with biased strong mixing data ⋮ Missing observations in observation-driven time series models ⋮ Learning rate of distribution regression with dependent samples ⋮ Nonparametric confidence intervals for location in time series data ⋮ Strong consistency of the internal estimator of nonparametric regression with dependent data ⋮ Partial functional linear regression with autoregressive errors ⋮ Dependent microstructure noise and integrated volatility estimation from high-frequency data ⋮ Empirical and sequential empirical copula processes under serial dependence ⋮ Conditional estimation for dependent functional data ⋮ A consistent nonparametric test for linearity of \(\text{AR} (p)\) models ⋮ Significance testing of rank cross-correlations between autocorrelated time series with short-range dependence ⋮ THE NUMBER OF PEAKS IN A STATIONARY SAMPLE AND ORTHANT PROBABILITIES ⋮ Multivariate CARMA processes, continuous-time state space models and complete regularity of the innovations of the sampled processes ⋮ Random Forests for Spatially Dependent Data ⋮ Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding ⋮ A copula spectral test for pairwise time reversibility ⋮ Permutation testing for dependence in time series ⋮ Multi‐purpose open‐end monitoring procedures for multivariate observations based on the empirical distribution function ⋮ Estimating beta-mixing coefficients via histograms ⋮ A central limit theorem for the Benjamini-Hochberg false discovery proportion under a factor model ⋮ Bootstrap rank tests for trend in time series ⋮ Sharp optimality for high-dimensional covariance testing under sparse signals ⋮ On estimation of nonparametric regression models with autoregressive and moving average errors ⋮ Quasi maximum likelihood estimation for strongly mixing state space models and multivariate Lévy-driven CARMA processes ⋮ Testing hypotheses on the ``drift of parameters in ARMA and ARCH models ⋮ Regularized least-squares regression: learning from a sequence ⋮ Semiparametrically weighted robust estimation of regression models ⋮ On the adaptive wavelet deconvolution of a density for strong mixing sequences ⋮ Missing not at random and the nonparametric estimation of the spectral density ⋮ Comments on ``Unbiased estimates for moments and cumulants in linear regression ⋮ Functional‐coefficient models under unit root behaviour ⋮ ARCHIMEDEAN COPULAS AND TEMPORAL DEPENDENCE ⋮ An improved method for forecasting spare parts demand using extreme value theory ⋮ Finite sample properties of system identification of ARX models under mixing conditions ⋮ Regression estimation under strong mixing data ⋮ Root-n-consistent estimation of partially linear time series models ⋮ On goodness-of-fit tests for weakly dependent processes using kernel method ⋮ On resampling and uncertainty estimation in linear system identification ⋮ Plug-in bandwidth choice in partial linear models with autoregressive errors ⋮ Bridge Estimation for Linear Regression Models with Mixing Properties ⋮ A note on the validity of cross-validation for evaluating autoregressive time series prediction ⋮ Efficient estimation of the mode of continuous multivariate data ⋮ Strong approximation of density estimators from weakly dependent observations by density estimators from independent observations ⋮ Towards a Unified Approach for Proving Geometric Ergodicity and Mixing Properties of Nonlinear Autoregressive Processes ⋮ Testing in partial linear regression models with dependent errors ⋮ Optimal change-point estimation in time series ⋮ Nonparametric kernel estimation of CVaR under \(\alpha\)-mixing sequences ⋮ Improved Seasonal Mann–Kendall Tests for Trend Analysis in Water Resources Time Series ⋮ Estimating tail decay for stationary sequences via extreme values ⋮ Empirical process theory for locally stationary processes ⋮ Wilks' theorem for semiparametric regressions with weakly dependent data ⋮ An asymptotic test for constancy of the variance under short-range dependence ⋮ Wavelet regression estimations with strong mixing data ⋮ Functional semiparametric partially linear model with autoregressive errors ⋮ Nonparametric estimation for stationary and strongly mixing processes on Riemannian manifolds ⋮ On residual sums of squares in non-parametric autoregression ⋮ Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes ⋮ The uniform consistency of sign estimate for the parameter of an AR(1)-model for observations with outliers

Cites Work

• Unnamed Item
• Some mixing properties of time series models
• Geometric ergodicity of Harris recurrent Markov chains with applications to renewal theory
• Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space
• Markovian representation of stochastic processes and its application to the analysis of autoregressive moving average processes
• SUR UN MODÉLE AUTORÉGRESSIF NON LINÉAIRE, ERGODICITÉ ET ERGODICITÉ GÉOMÉTRIQUE
• Mixing properties of harris chains and autoregressive processes
• Conditions for linear processes to be strong-mixing
• Mixing Conditions for Markov Chains
• On the Strong Mixing Property for Linear Sequences

This page was built for publication: Mixing properties of ARMA processes