Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/16439
Title: Improved local quantile regression
Authors: Yu, K
Liu, X
Keywords: Bandwidth Selection;Nonparametric Quantile Regression;Quantile
Issue Date: 2018
Publisher: SAGE Publications
Citation: Statistical Modelling: an international journal
Abstract: We investigate a new kernel-weighted likelihood smoothing quantile regression method. The likelihood is based on a normal scale-mixture representation of asymmetric Laplace distribution (ALD). This approach enjoys the same good design adaptation as the local quantile regression (Spokoiny et al., 2013), particularly for smoothing extreme quantile curves, and ensures non-crossing quantile curves for any given sample. The performance of the proposed method is evaluated via extensive Monte Carlo simulation studies and one real data analysis.
URI: http://bura.brunel.ac.uk/handle/2438/16439
ISSN: 1471-082X
Appears in Collections:Dept of Mathematics Research Papers

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