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Recent Developments in Location Estimation and Regression for Long-Memory Processes

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New Directions in Time Series Analysis

Part of the book series: The IMA Volumes in Mathematics and its Applications ((IMA,volume 46))

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Abstract

The problem of long-range dependence in statistical applications has been known to scientists and applied statisticians long before suitable models were known. Parsimonious models with such behaviour are stationary processes with non-summable correlations. Many classical limit theorems do not hold for these processes and rates of convergence are slower than under independence or weak dependence. Therefore, for many statistics, usual confidence intervals are too small by a factor which tends to infinity with increasing sample size. In this paper we give a survey of recent results on point and interval estimation of location and of the coefficients in parametric linear regression, as well as nonparametric regression.

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Author information

Authors and Affiliations

  1. Department of Statistics, Texas A&M University, College Station, TX, 77843, USA

    Jan Beran

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  1. Jan Beran
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Editor information

Editors and Affiliations

  1. Department of Statistics, University of California, Berkeley, CA, 94720, USA

    David Brillinger

  2. Department of Electrical Engineering, McGill University, Montreal, Quebec, H3A 2A7, Canada

    Peter Caines

  3. Department of Economics, University of Minnesota, Minneapolis, MN, 55455, USA

    John Geweke

  4. Department of Statistics, Texas A&M University, College Station, TX, 77843-3143, USA

    Emanuel Parzen

  5. Mathematics Department, University of California, San Diego, La Jolla, CA, 92093, USA

    Murray Rosenblatt

  6. Department of Mathematics, Boston University, Boston, MA, 02215, USA

    Murad S. Taqqu

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© 1993 Springer-Verlag New York, Inc.

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Beran, J. (1993). Recent Developments in Location Estimation and Regression for Long-Memory Processes. In: Brillinger, D., Caines, P., Geweke, J., Parzen, E., Rosenblatt, M., Taqqu, M.S. (eds) New Directions in Time Series Analysis. The IMA Volumes in Mathematics and its Applications, vol 46. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-9296-5_1

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  • DOI: https://doi.org/10.1007/978-1-4613-9296-5_1

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4613-9298-9

  • Online ISBN: 978-1-4613-9296-5

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