Recent Developments of Threshold Estimation for Nonlinear Time Series

Author:

Hang Ngai1,Kutoyants Yury A.2

Affiliation:

1. Department of Statistics, The Chinese University of Hong Kong, Shatin, NT, Hong Kong. Email: nhchan@sta.cuhk.edu.hk

2. Laboratoire de Statistique et Processus, Université du Maine, 70285 Le Mans, Cedix 9, France. Email: Yury.Kutoyants@univ-lemans.fr

Publisher

The Japan Statistical Society

Reference36 articles.

1. (1)An, H. Z. and Huang, F. C. (1996). The geometric ergodicity of nonlinear autoregressive models, Statistica Sinica, 6, 943–956.

2. (2)Bradley, R. C. (2007). Introduction to Strong Mixing Conditions, Vol. 1, Kendrick Press, Heber City.

3. (3)Brockwell, R. J. (1994). On continuous time threshold ARMA processes, J. Stat. Plann. Inference, 39, 291–304.

4. (4)Chan, K. S. (1993). Consistency and limiting distribution of the LSE of a TAR, Ann. Stat., 21, 520–533.

5. (5)Chan, K. S. (2010). Exploration of a Nonlinear World: An Appreciation of Howell Tong's Contributions to Statistics, World Scientific, Singapore.

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1. On cusp location estimation for perturbed dynamical systems;Scandinavian Journal of Statistics;2019-03-10

2. Estimation in threshold autoregressive models with correlated innovations;Annals of the Institute of Statistical Mathematics;2013-04-07

3. On nonlinear TAR processes and threshold estimation;Mathematical Methods of Statistics;2012-04

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