Asymptotics of a class of markov processes generated by $X_{n+1}=f(X_n)+\epsilon_{n+1}$

  • Lee, Oe-Sook (Department of Statistics, Ewha Womens University, Seoul 120-750)
  • Published : 1994.06.01

Abstract

We consider the markov process ${X_n}$ on R which is genereated by $X_{n+1} = f(X_n) + \epsilon_{n+1}$. Sufficient conditions for irreducibility and geometric ergodicity are obtained for such Markov processes. In additions, when ${X_n}$ is geometrically ergodic, the functional central limit theorem is proved for every bounded functions on R.

Keywords

References

  1. Annals of Probability v.16 Asymptotics of a class of Markov processes which are not in general irreducible Bhattacharya,R.N.;Lee,O.
  2. Convergence of probability measures Billingsley,P.
  3. Probability Breiman,L.
  4. Journal of Applied Probability v.22 A multiplethreshold AR(1) model Chan,K.;Petruccelli,J.;Tong,H.;Woolford,S.
  5. Annals of Probability v.3 A uniform theory for sums of Markov chain transition probabilities Cogburn,R.
  6. Soviet Math. Dokl. v.19 no.2 The central limit theorem for stationary Markov processes Gordin,M.I.;Lifsic,B.A.
  7. Gebiete v.8 Contributions to Doeblin’s theory of Markov Processes Jain,N.;Jamison,B.
  8. Ph. D. thesis, Indiana University Lee,C.
  9. Communications of Korean Mathematical Society v.3 no.2 Sufficient conditions for irrducibility, ergodicity and recurrence of a Markov proces $X{n+1}=f(X_n)+\epsilon_{n+1}$ Lee,O.
  10. Sotchastic Processes and their Applications v.12 Geometric ergodicity of Harris recurrent Markov chains with applications to renewal theory Nummelin,E.;Tuominen,P.
  11. Limit theorems for Markov chain transition probabilities Orey,S.
  12. Non-linear time series Tong,H.
  13. Sotchastic Processes and their Applications v.3 Sufficient conditions for ergodicity and recurrence of Markov chains on a general state space Tweedie,R.L.