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On spline regression under Gaussian subordination with long memory

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  • Beran, Jan
  • Weiershäuser, Arno

Abstract

Motivated by an example from neurobiology, we consider estimation in a spline regression model with long-range dependent errors that are generated by Gaussian subordination. Consistency and the asymptotic distribution are derived for general Hermite ranks. Simulations illustrate the asymptotic results and finite sample properties. The method is applied to optical measurements of calcium concentration in the antennal lobe of honey bees used in the study of olfactory patterns.

Suggested Citation

  • Beran, Jan & Weiershäuser, Arno, 2011. "On spline regression under Gaussian subordination with long memory," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 315-335, February.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:2:p:315-335
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    References listed on IDEAS

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    5. Jasdan Joerges & Armin Küttner & C. Giovanni Galizia & Randolf Menzel, 1997. "Representations of odours and odour mixtures visualized in the honeybee brain," Nature, Nature, vol. 387(6630), pages 285-288, May.
    6. Koul, Hira L. & Baillie, Richard T., 2003. "Asymptotics of M-estimators in non-linear regression with long memory designs," Statistics & Probability Letters, Elsevier, vol. 61(3), pages 237-252, February.
    7. Hall, Peter & Hart, Jeffrey D., 1990. "Nonparametric regression with long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 36(2), pages 339-351, December.
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    Cited by:

    1. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.

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