New Bandwidth Selection for Kernel Quantile Estimators | Zendy

Ali Alkenani | Zendy; Keming Yu | Zendy

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New Bandwidth Selection for Kernel Quantile Estimators

Author(s) -

Ali Alkenani,

Keming Yu

Publication year - 2012

Publication title -

journal of probability and statistics

Language(s) - English

Resource type - Journals

eISSN - 1687-9538

pISSN - 1687-952X

DOI - 10.1155/2012/138450

Subject(s) - estimator , bandwidth (computing) , smoothing , quantile , mathematics , kernel (algebra) , mathematical optimization , kernel smoother , kernel density estimation , mean squared error , computer science , kernel method , statistics , artificial intelligence , telecommunications , combinatorics , radial basis function kernel , support vector machine

We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a mean integrated squared error curve of which the minimising value determines an optimal bandwidth. This method is shown to lead to asymptotically optimal bandwidth choice and we also provide some general theory on the performance of optimal, data-based methods of bandwidth choice. The numerical performances of the proposed methods are compared in simulations, and the new bandwidth selection is demonstrated to work very well

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