Abstract
The main object of this work is to show that some theoretical results concerning autoregressive processes [4] are indeed applicable: first, we choose discretization parameters for the computation of non parametric kernel estimators for these processes; then, we investigate some “bad” cases and some “good” cases; it seems that effective computations generally give better results than those obtained in theory, finally we study the relation between the deterministic case of iterations and the non-deterministic case of autoregressive process. In addition, we describe the behaviour of the invariant measures associated with the relevent process when there is little white noise.
This work was supported by the biometry laboratory of I.N.R.A., Jouy en Josas 78, France.
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References
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© 1983 Springer-Verlag New York Inc.
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Doukhan, P. (1983). Simulation in the General First Order Autoregressive Process (Unidimensional Normal Case). In: Florens, J.P., Mouchart, M., Raoult, J.P., Simar, L., Smith, A.F.M. (eds) Specifying Statistical Models. Lecture Notes in Statistics, vol 16. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-5503-1_4
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DOI: https://doi.org/10.1007/978-1-4612-5503-1_4
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