期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
卷 74, 期 -, 页码 203-221出版社
OXFORD UNIV PRESS
DOI: 10.1111/j.1467-9868.2011.01002.x
关键词
Biclustering; Microarrary gene expression data; Multivariate regression; Oracle property; Regularization
资金
- US National Science Foundation [NSF-0934617]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0934617] Funding Source: National Science Foundation
. For a reduced rank multivariate stochastic regression model of rank r*, the regression coefficient matrix can be expressed as a sum of r* unit rank matrices each of which is proportional to the outer product of the left and right singular vectors. For improving predictive accuracy and facilitating interpretation, it is often desirable that these left and right singular vectors be sparse or enjoy some smoothness property. We propose a regularized reduced rank regression approach for solving this problem. Computation algorithms and regularization parameter selection methods are developed, and the properties of the new method are explored both theoretically and by simulation. In particular, the regularization method proposed is shown to be selection consistent and asymptotically normal and to enjoy the oracle property. We apply the proposed model to perform biclustering analysis with microarray gene expression data.
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