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|Title:||Improved local quantile regression|
|Keywords:||Bandwidth Selection;Nonparametric Quantile Regression;Quantile|
|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.|
|Appears in Collections:||Dept of Mathematics Research Papers|
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