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Local linear double and asymmetric kernel estimation of conditional quantiles

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  • Muhammad Anas Knefati
  • Abderrahim Oulidi
  • Belkacem Abdous

Abstract

In this work, we propose and investigate a family of non parametric quantile regression estimates. The proposed estimates combine local linear fitting and double kernel approaches. More precisely, we use a Beta kernel when covariate’s support is compact and Gamma kernel for left-bounded supports. Finite sample properties together with asymptotic behavior of the proposed estimators are presented. It is also shown that these estimates enjoy the property of having finite variance and resistance to sparse design.

Suggested Citation

  • Muhammad Anas Knefati & Abderrahim Oulidi & Belkacem Abdous, 2016. "Local linear double and asymmetric kernel estimation of conditional quantiles," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(12), pages 3473-3488, June.
    • Handle: RePEc:taf:lstaxx:v:45:y:2016:i:12:p:3473-3488
    DOI: 10.1080/03610926.2014.889923
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