A Lasso-type Robust Variable Selection for Time-Course Microarray Data |
| |
Authors: | Ji Young Kim |
| |
Institution: | 1. Department of Mathematics and Statistics, Mount Holyoke College, South Hadley, Massachusetts, USAjiykim@mtholyoke.edu |
| |
Abstract: | 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. |
| |
Keywords: | Robust Lasso Multivariate response Time-course gene expression |
|
|