Noncrossing varying coefficient support vector quantile regression
Jooyong Shim, Changha Hwang, Insuk Sohn, Kyungha Seok · Journal of the Korean Data and Information Science Society · 2020
Quantile regression fits specified percentiles of the response, such as the 90th percentile, and can potentially describe the entire conditional distribution of the response. Sometimes quantile functions estimated at different quantiles can cross each other. Varying coefficient models are a useful extension of classical linear models. We propose a new noncrossing varying coefficient support vector quantile regression method based on a location-scale model. To choose the hyper-parameters we apply the model selection method that use cross validation techniques. The proposed method provides a good solution for estimating noncrossing quantile regression functions when several quantiles are required. Real examples are provided to show the usefulness of the proposed method.