Title: Improved local quantile regression
Authors: Keming Yu - Brunel University (United Kingdom) [presenting]
Abstract: A new kernel-weighted likelihood smoothing quantile regression method is investigated. 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, particularly, for smoothing extreme quantile curves, and it ensures non-crossing quantile curves for any given sample. An application for the connections among film characteristics from the Internet Movie Database (IMDb) is promising.