A Lasso-type Robust Variable Selection for Time-Course Microarray Data

Ji Young Kim

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 7, 1411-1425

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:44:y:2015:i:7:p:1411-1425

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DOI: 10.1080/03610926.2013.770531

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