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The Bahadur representation for sample quantile under NOD sequence

Author

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  • Xiaoqin Li
  • Wenzhi Yang
  • Shuhe Hu
  • Xuejun Wang

Abstract

In this paper, we investigate the Bahadur representation of sample quantile based on negatively orthant dependent sequence, which is weaker than negatively associated sequence. Our results extend and improve the results of Ling [(2008), ‘The Bahadur Representation for Sample Quantiles Under Negatively Associated Sequence’, Statistics & Probability Letters, 78, 2660–2663].

Suggested Citation

  • Xiaoqin Li & Wenzhi Yang & Shuhe Hu & Xuejun Wang, 2011. "The Bahadur representation for sample quantile under NOD sequence," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(1), pages 59-65.
    • Handle: RePEc:taf:gnstxx:v:23:y:2011:i:1:p:59-65
    DOI: 10.1080/10485252.2010.486033
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    Cited by:

    1. Xuejun Wang & Yi Wu & Wei Yu & Wenzhi Yang & Shuhe Hu, 2019. "Asymptotics for the linear kernel quantile estimator," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1144-1174, December.
    2. Qinchi Zhang & Wenzhi Yang & Shuhe Hu, 2014. "On Bahadur representation for sample quantiles under α-mixing sequence," Statistical Papers, Springer, vol. 55(2), pages 285-299, May.
    3. Santanu Dutta & Tushar Kanti Powdel, 2023. "Modeling Long Term Return Distribution and Nonparametric Market Risk Estimation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 257-289, May.

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