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Asymptotic properties of nonparametric quantile estimation with spatial dependency

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Listed:
  • S.‐H. Arnaud Kanga
  • Ouagnina Hili
  • Sophie Dabo‐Niang
  • Assi N'Guessan

Abstract

The purpose of this work is to nonparametrically estimate the conditional quantile for a locally stationary multivariate spatial process. The new kernel quantile estimate derived from the one of conditional distribution function (CDF). The originality in the paper is based on the ability to take into account some local spatial dependency in estimate CDF form. Consistency and asymptotic normality of the estimates are obtained under α$$ \alpha $$‐mixing condition. Numerical study and application to real data are given in order to illustrate the performance of our methodology.

Suggested Citation

  • S.‐H. Arnaud Kanga & Ouagnina Hili & Sophie Dabo‐Niang & Assi N'Guessan, 2023. "Asymptotic properties of nonparametric quantile estimation with spatial dependency," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(3), pages 254-283, August.
    • Handle: RePEc:bla:stanee:v:77:y:2023:i:3:p:254-283
    DOI: 10.1111/stan.12284
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    References listed on IDEAS

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