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Statistical diagnosis for non-parametric regression models with random right censorship based on the empirical likelihood method

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  • Shuling Wang
  • Xiaoyan Wang
  • Jiangtao Dai

Abstract

In this paper, we consider statistical diagnostic for non-parametric regression models with right-censored data based on empirical likelihood. First, the primary model is transformed to the non-parametric regression model. Then, based on empirical likelihood methodology, we define some diagnostic statistics. At last, some simulation studies show that our proposed procedure can work fairly well.

Suggested Citation

  • Shuling Wang & Xiaoyan Wang & Jiangtao Dai, 2015. "Statistical diagnosis for non-parametric regression models with random right censorship based on the empirical likelihood method," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1367-1373, June.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1367-1373
    DOI: 10.1080/02664763.2014.999656
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    References listed on IDEAS

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    1. Hongtu Zhu & Joseph G. Ibrahim & Niansheng Tang & Heping Zhang, 2008. "Diagnostic measures for empirical likelihood of general estimating equations," Biometrika, Biometrika Trust, vol. 95(2), pages 489-507.
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    Cited by:

    1. Farroukh, Arafet & Mazioued, Manel & Pédussel Wu, Jennifer, 2024. "Revisiting the linkage between remittances inflow and economic growth: A semi-parametric estimation with panel data," IPE Working Papers 238/2024, Berlin School of Economics and Law, Institute for International Political Economy (IPE).

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