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Modified kernel regression estimation with functional time series data

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  • Ling, Nengxiang
  • Wang, Chao
  • Ling, Jin

Abstract

This paper derives the asymptotic distribution of a modified kernel regression estimator for strong mixing functional time series data. As a direct consequence, the approximate pointwise confidence interval of regression operator is presented.

Suggested Citation

  • Ling, Nengxiang & Wang, Chao & Ling, Jin, 2016. "Modified kernel regression estimation with functional time series data," Statistics & Probability Letters, Elsevier, vol. 114(C), pages 78-85.
    • Handle: RePEc:eee:stapro:v:114:y:2016:i:c:p:78-85
    DOI: 10.1016/j.spl.2016.03.009
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    References listed on IDEAS

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    1. Masry, Elias, 2005. "Nonparametric regression estimation for dependent functional data: asymptotic normality," Stochastic Processes and their Applications, Elsevier, vol. 115(1), pages 155-177, January.
    2. Benhenni, K. & Hedli-Griche, S. & Rachdi, M. & Vieu, P., 2008. "Consistency of the regression estimator with functional data under long memory conditions," Statistics & Probability Letters, Elsevier, vol. 78(8), pages 1043-1049, June.
    3. M'hamed Ezzahrioui & Elias Ould Saïd, 2010. "Some asymptotic results of a non‐parametric conditional mode estimator for functional time‐series data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 64(2), pages 171-201, May.
    4. Laib, Naâmane & Louani, Djamal, 2010. "Nonparametric kernel regression estimation for functional stationary ergodic data: Asymptotic properties," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2266-2281, November.
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