Local linear double and asymmetric kernel estimation of conditional quantiles

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 support...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2016-06, Vol.45 (12), p.3473-3488
Main Authors: Knefati, Muhammad Anas, Oulidi, Abderrahim, Abdous, Belkacem
Format: Article
Language:English
Subjects:
62G08, 62G05 and 62G20
Asymmetric kernels
Asymmetry
Asymptotic properties
Beta kernels
Double kernel conditional quantile estimation
Economic models
Estimates
Gamma kernels
Kernels
Mathematical analysis
Quantile regression
Quantiles
Regression
Regression analysis
Statistics
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Summary: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.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2014.889923