Mixing properties of harris chains and autoregressive processes
From MaRDI portal
Publication:3761424
DOI10.2307/3214462zbMath0623.60087OpenAlexW4235362874MaRDI QIDQ3761424
Sastry G. Pantula, Krishna B. Athreya
Publication date: 1986
Published in: Journal of Applied Probability (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.2307/3214462
Stationary stochastic processes (60G10) Discrete-time Markov processes on general state spaces (60J05)
Related Items (39)
From reflected Lévy processes to stochastically monotone Markov processes via generalized inverses and supermodularity ⋮ A note on strong mixing of ARMA processes ⋮ Rare events for stationary processes. ⋮ Extremal clustering in non-stationary random sequences ⋮ Conditions for finite moments of waiting times in \(G/G/1\) queues ⋮ Basic structure of the asymptotic theory in dynamic nonlineaerco nometric models, part i: consistency and approximation concepts ⋮ On consistency of minimum description length model selection for piecewise autoregressions ⋮ Regularized least square regression with dependent samples ⋮ Mixing properties of ARMA processes ⋮ Minimum distance estimation in linear regression with strong mixing errors ⋮ Regularized least square regression with unbounded and dependent sampling ⋮ Frequency polygons for weakly dependent processes ⋮ Multivariate CARMA processes, continuous-time state space models and complete regularity of the innovations of the sampled processes ⋮ Regression learning with non-identically and non-independently sampling ⋮ Mixing properties of ARCH and time-varying ARCH processes ⋮ Regenerative block-bootstrap confidence intervals for tail and extremal indexes ⋮ Kernel density estimation for random fields: TheL1Theory ⋮ Testing for stationarity-ergodicity and for comovements between nonlinear discrete time Markov processes ⋮ Comments on ``Unbiased estimates for moments and cumulants in linear regression ⋮ Testing Independence in Linear Process with Non-Normal Innovations ⋮ Prediction of time series by statistical learning: general losses and fast rates ⋮ Convergence of Hermite Series Density Estimators Under Conditions of Weak Dependence ⋮ A regularity condition and a limit theorem for Harris ergodic Markov chains ⋮ Recurrence properties of autoregressive processes with super-heavy-tailed innovations ⋮ Stationarity and mixing properties of the dynamic Tobit model ⋮ The Numerical Invariant Measure of Stochastic Differential Equations With Markovian Switching ⋮ The robust focused information criterion for strong mixing stochastic processes with \(\mathscr{L}^2\)-differentiable parametric densities ⋮ Fixed-design regression for linear time series ⋮ Unnamed Item ⋮ Large deviations for sum of UEND andφ-mixing random variables with heavy tails ⋮ A note on application of integral operator in learning theory ⋮ Rank statistics for serial dependence ⋮ Estimating tail decay for stationary sequences via extreme values ⋮ An extension of almost sure central limit theorem for self-normalized products of sums for mixing sequences ⋮ Non-asymptotic tests of model performance ⋮ Nonparametric estimation for dependent data ⋮ A Liapounov bound for solutions of the Poisson equation ⋮ Similarity-based model for ordered categorical data ⋮ Geometric absolute regularity of Banach space-valued autoregressive processes.
This page was built for publication: Mixing properties of harris chains and autoregressive processes
