TY - JOUR
T1 - Improved local quantile regression
AU - Liu, Xi
AU - Yu, Keming
AU - Xu, Qifa
AU - Tang, Xueqing
PY - 2018/7/9
Y1 - 2018/7/9
N2 - 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.
AB - 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.
U2 - 10.1177/1471082X18782057
DO - 10.1177/1471082X18782057
M3 - Article
SN - 1477-0342
JO - Statistical Modelling
JF - Statistical Modelling
ER -