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A note on quantile estimation for long-range dependent stochastic processes


  • Youndjé, É.
  • Vieu, P.


This note investigates the consistency properties of the kernel-type estimator of a quantile, in the setting of a long memory stationary stochastic process. Under a general long-range dependence situation (without any restriction of gaussian type) we give consistency results, and rates of convergence. An interesting by-product of this paper is a new consistency result for kernel-type estimator of a smooth distribution function (with rates) over the whole real line.

Suggested Citation

  • Youndjé, É. & Vieu, P., 2006. "A note on quantile estimation for long-range dependent stochastic processes," Statistics & Probability Letters, Elsevier, vol. 76(2), pages 109-116, January.
  • Handle: RePEc:eee:stapro:v:76:y:2006:i:2:p:109-116

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    References listed on IDEAS

    1. Giraitis, Liudas & Koul, Hira L. & Surgailis, Donatas, 1996. "Asymptotic normality of regression estimators with long memory errors," Statistics & Probability Letters, Elsevier, vol. 29(4), pages 317-335, September.
    2. Hall, Peter & Hart, Jeffrey D., 1990. "Nonparametric regression with long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 36(2), pages 339-351, December.
    3. Cai, Zongwu & Roussas, George G., 1997. "Smooth estimate of quantiles under association," Statistics & Probability Letters, Elsevier, vol. 36(3), pages 275-287, December.
    4. Robinson, Peter M., 1997. "Large-sample inference for nonparametric regression with dependent errors," LSE Research Online Documents on Economics 302, London School of Economics and Political Science, LSE Library.
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    Cited by:

    1. Lihong Wang, 2010. "Kernel type smoothed quantile estimation under long memory," Statistical Papers, Springer, vol. 51(1), pages 57-67, January.
    2. Ling, Nengxiang, 2008. "The Bahadur representation for sample quantiles under negatively associated sequence," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2660-2663, November.


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