Variable selection on the mixture of additive quantile regression models.
| Autor: | Wei-Te Lin, 林唯德 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 When observations come from the mixture of additive quantile regression models, some unreasonable results of variable selection could happen if the existing quantile approaches are applied directly. In this work, we attempt to develop an algorithm to cluster data, select relevant variables, and identify the related structures simultaneously. In the proposed algorithm, B-spline function is utilized to approximate the additive model and the quantile regression with the Lasso-type penalty is employed for the variable selection and structure detection. The performance of the suggested algorithm is discussed through simulation problems. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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