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Nonparametric regression estimation in models with weak error's structure

Author

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  • Fraiman, Ricardo
  • Iribarren, Gonzalo Pérez

Abstract

In this paper we propose nonparametric estimates of the regression function and its derivative when it is only assumed a weak error's structure. We study their local and global asymptotic behaviour when we observe dependent trajectories.

Suggested Citation

  • Fraiman, Ricardo & Iribarren, Gonzalo Pérez, 1991. "Nonparametric regression estimation in models with weak error's structure," Journal of Multivariate Analysis, Elsevier, vol. 37(2), pages 180-196, May.
    • Handle: RePEc:eee:jmvana:v:37:y:1991:i:2:p:180-196
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    Citations

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    Cited by:

    1. Boente, Graciela & Fraiman, Ricardo, 2000. "Kernel-based functional principal components," Statistics & Probability Letters, Elsevier, vol. 48(4), pages 335-345, July.
    2. Xuejun Wang & Xin Deng & Fengxi Xia & Shuhe Hu, 2017. "The consistency for the estimators of semiparametric regression model based on weakly dependent errors," Statistical Papers, Springer, vol. 58(2), pages 303-318, June.
    3. Xuejun Wang & Yi Wu & Rui Wang & Shuhe Hu, 2021. "On consistency of wavelet estimator in nonparametric regression models," Statistical Papers, Springer, vol. 62(2), pages 935-962, April.
    4. Zhou, Xing-cai & Lin, Jin-guan, 2012. "A wavelet estimator in a nonparametric regression model with repeated measurements under martingale difference error’s structure," Statistics & Probability Letters, Elsevier, vol. 82(11), pages 1914-1922.

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