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Statistical estimation of nonstationaryGaussian processes with long-range dependence and intermittency

Listed author(s):
  • Gao, jiti
  • Anh, vo
  • Heyde, christopher
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This paper considers statistical inference for nonstationaryGaussian processes with long-range dependence and intermittency. The existence of such a process has been established by Anh et al. (J. Statist. Plann. Inference 80 (1999) 95–110). We systematically consider the case where the spectral densityof nonstationaryGaussian processes with stationaryincrements is of a general and

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11972.

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Date of creation: 13 Dec 1999
Date of revision: 23 Oct 2001
Publication status: Published in Stochastic Processes and Their Applications 1.99(2002): pp. 295-323
Handle: RePEc:pra:mprapa:11972
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  1. M. C. Viano & Cl. Deniau & G. Oppenheim, 1995. "Long-Range Dependence And Mixing For Discrete Time Fractional Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(3), pages 323-338, May.
  2. Viano, M. C. & Deniau, C. & Oppenheim, G., 1994. "Continuous-time fractional ARMA processes," Statistics & Probability Letters, Elsevier, vol. 21(4), pages 323-336, November.
  3. Heyde, C. C. & Gay, R., 1993. "Smoothed periodogram asymptotics and estimation for processes and fields with possible long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 45(1), pages 169-182, March.
  4. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
  5. Robinson, Peter M., 1997. "Large-sample inference for nonparametric regression with dependent errors," LSE Research Online Documents on Economics 302, London School of Economics and Political Science, LSE Library.
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