EcoSta 2018: Registration
View Submission - EcoSta2018
Title: Variable selection on the mixture of additive quantile regressions model Authors:  Wei-Te Lin - National Dong Hwa University (Taiwan) [presenting]
Wei-Ying Wu - National Dong Hwa University (Taiwan)
Abstract: When observations come from the mixture of additive quantile regressions model, some unreasonable results of variable selection could happen if the existing quantile approaches are applied directly. We attempt to develop an algorithm to cluster data, select relevant variables, and identify the related structures simultaneously. In the developed algorithm, B-spline function is utilized to approximate the additive model and the quantile regression with the adaptively weighted group Lasso penalty is employed for the variable selection and structure detection. The performance of the proposed algorithm is discussed through simulation problems.