Mixing properties of harris chains and autoregressive processes
- 1 December 1986
- journal article
- Published by Cambridge University Press (CUP) in Journal of Applied Probability
- Vol. 23 (4), 880-892
- https://doi.org/10.2307/3214462
Abstract
Let {Yn: n ≧ 1} be a Harris-recurrent Markov chain on a general state space. It is shown that {Yn} is strong mixing, provided there exists a stationary probability distribution π (·) for {Yn}. Necessary and sufficient conditions for an autoregressive process to be uniform mixing are given.Keywords
This publication has 9 references indexed in Scilit:
- Non-strong mixing autoregressive processesJournal of Applied Probability, 1984
- Non-strong mixing autoregressive processesJournal of Applied Probability, 1984
- Nonlinear Regression with Dependent ObservationsEconometrica, 1984
- Least Squares Estimates in Stochastic Regression Models with Applications to Identification and Control of Dynamic SystemsThe Annals of Statistics, 1982
- Conditions for linear processes to be strong-mixingProbability Theory and Related Fields, 1981
- A new approach to the limit theory of recurrent Markov chainsTransactions of the American Mathematical Society, 1978
- Strong mixing properties of linear stochastic processesJournal of Applied Probability, 1974
- Strong mixing properties of linear stochastic processesJournal of Applied Probability, 1974
- Markov Processes. Structure and Asymptotic BehaviorPublished by Springer Science and Business Media LLC ,1971