Robustness of Forecasting for Autoregressive Time Series with Bilinear Distortions

Authors

  • Yuriy Kharin Belarusian State University, Minsk
  • Olga Radzieuskaya Belarusian State University, Minsk

DOI:

https://doi.org/10.17713/ajs.v37i1.287

Abstract

The paper is devoted to the investigation of bilinear stochastic time series model BL(p, 0, 1, 1). The linear autoregressive forecasting statistic is considered under the mean-square risk criterion; its robustness under bilinear distortions is evaluated.

References

Anderson, T. W. (1994). The Statistical Analysis of Time Series. New York: Wiley-Interscience.

Fan, J., and Yao, Q. (2003). Nonlinear Time Series. Nonparametric and Parametric Methods. New York: Springer-Verlag.

Granger, C., and Andersen, A. (1978). An Introduction to Bilinear Time Series Models. Gottingen: Vandenhoek and Ruprecht.

Kharin, Y. (1996). Robustness in Statistical Pattern Recognition. Dordrecht, Boston, London: Kluwer Academic Publishers.

Rao, S., and Gabr, M. (1984). An Introduction to Bispectral Analysis and Bilinear Time Series Models. New York: Springer-Verlag.

Terdik, G. (1999). Bilinear Stochastic Models and Related Problems of Nonlinear Time Series Analysis: A Frequency Domain Approach. New York: Springer-Verlag.

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Published

2016-04-03

How to Cite

Kharin, Y., & Radzieuskaya, O. (2016). Robustness of Forecasting for Autoregressive Time Series with Bilinear Distortions. Austrian Journal of Statistics, 37(1), 61–70. https://doi.org/10.17713/ajs.v37i1.287

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Section

Articles