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Wavelet estimation in varying coefficient models for censored dependent data

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  • Zhou, Xing-cai
  • Xu, Ying-zhi
  • Lin, Jin-guan

Abstract

In this paper, we discuss the estimation of varying coefficient models based on censored data by wavelet technique when the survival and the censoring times are from a stationary α-mixing sequence. For the wavelet estimator of varying coefficient functions, the strong uniform convergence rate is derived and the asymptotic normality is established under the mild conditions. The strong uniform convergence rate we obtained is comparable with the optimal convergence rate of the nonparametric estimation in nonparametric models.

Suggested Citation

  • Zhou, Xing-cai & Xu, Ying-zhi & Lin, Jin-guan, 2017. "Wavelet estimation in varying coefficient models for censored dependent data," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 179-189.
    • Handle: RePEc:eee:stapro:v:122:y:2017:i:c:p:179-189
    DOI: 10.1016/j.spl.2016.11.009
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    References listed on IDEAS

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    Cited by:

    1. Xingcai Zhou & Guang Yang & Yu Xiang, 2022. "Quantile-Wavelet Nonparametric Estimates for Time-Varying Coefficient Models," Mathematics, MDPI, vol. 10(13), pages 1-15, July.

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