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摘要
Lasso has been widely used for variable selection because of its sparsity, and a number of its extensions have been developed. In this article, we propose a robust variant of Lasso for the time-course multivariate response, and develop an algorithm which transforms the optimization into a sequence of ridge regressions. The proposed method enables us to effectively handle multivariate responses and employs a basis representation of the regression parameters to reduce the dimensionality. We assess the proposed method through simulation and apply it to the microarray data.
- 出版日期2015
