Improved local quantile regression. (October 2019)

• Record Type: Journal Article
• Authors: Liu, Xi; Yu, Keming; Xu, Qifa; Tang, Xueqing
• Is Part Of: Statistical modelling. Volume 19:Number 5(2019)
• Page Start: 501
• Page End: 523
• Publication Date: 2019-10
• Subjects: Bandwidth selection -- asymmetric Laplace distribution -- non-parametric quantile regression -- propagation condition -- quantile crossing; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X18782057
• Languages: English
• ISSNs: 1471-082X

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, Journal of Statistical Planning and Inference, 143, 1109–1129), 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.