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Abstrakty
We consider the partial sum process of a bounded functional of a linear process and the linear process has no finite mean. We assume the innovations of the linear process are independent and identically distributed and that the distribution of the innovations belongs to the domain of attraction of an α-stable law and satisfies some additional assumptions. Then we establish the finite-dimensional convergence in distribution of the partial sum process to a stable Lévy motion.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
337--351
Opis fizyczny
Bibliogr. 16 poz.
Twórcy
autor
- Graduate School of Economics, Hitotsubashi University Kunitachi, Tokyo 186-8601, Japan
Bibliografia
- [1] P. Billingsley, Convergence of Probability Measures, Wiley, New York 1968.
- [2] P. Embrechts, C. Klüppelberg and T. Mikosch, Modelling Extremal Events for Insurance and Finance, Springer, Berlin-Heidelberg 1997.
- [3] L. Giraitis, H. L. Koul and D. Surgailis, Asymptotic normality of regression estimators with long memory errors, Statist. Probab. Lett. 29 (1996), pp. 317-335.
- [4] E. J. Hannan, The central limit theorem for time series regression, Stochastic Process. Appl. 9 (1979), pp. 281-289.
- [5] H.-C. Ho and T. Hsing, On the asymptotic expansion of the empirical process of longmemory moving averages, Ann. Statist. 24 (1996), pp. 992-1024.
- [6] H.-C. Ho and T. Hsing, Limit theorems for functionals of moving averages, Ann. Probab. 25 (1997), pp. 1636-1669.
- [7] T. Honda, Noncentral limit theorems for bounded functions of linear processes without finite mean, Discussion paper #2006-22, Graduate School of Economics, Hitotsubashi Univ.
- [8] T. Hsing, On the asymptotic distribution of partial sum of functionals of infinite-variance moving averages, Ann. Probab. 27 (1999), pp. 1579-1599.
- [9] H. L. Koul and D. Surgailis, Asymptotics of empirical processes of long memory moving averages with infinite variance, Stochastic Process. Appl. 91 (2001), pp. 309-336.
- [10] H. L. Koul and D. Surgailis, Asymptotic expansion of the empirical process of long memory moving averages, in: Empirical Process Techniques for Dependent Data, H. Dehling, T. Mikosch and M. Sørensen (Eds.), Birkhäuser, Boston 2002, pp. 213-239.
- [11] V. Pipiras and M. S. Taqqu, Central limit theorems for partial sums of bounded functionals of infinite-variance moving averages, Bernoulli 9 (2003), pp. 833-855.
- [12] G. Samorodnitsky and M. S. Taqqu, Stable Non-Gaussian Processes: Stochastic Models with Infinite Variance, Chapman and Hall, London 1994.
- [13] D. Surgailis, Stable limits of empirical processes of moving averages with infinite variance, Stochastic Process. Appl. 100 (2002), pp. 255-274.
- [14] D. Surgailis, Stable limits of sums of bounded functions of long-memory moving averages with finite variance, Bernoulli 10 (2004), pp. 327-355.
- [15] B. von Bahr and C.-G.Esseen, Inequalities for the rth absolute moment of the sum of random variables, 1≤r≤2, Ann. Math. Statist. 36 (1965), pp. 299-303.
- [16] W. B. Wu, Additive functionals of infinite-variance moving averages, Statist. Scinica 13 (2003), pp. 1259-1267.
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Bibliografia
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