Skip to main content

Reduced rank stochastic regression with a sparse singular value decomposition

Buy Article:

$51.00 plus tax (Refund Policy)

Abstract:

Summary.  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.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1467-9868.2011.01002.x

Affiliations: 1: Kansas State University, Manhattan, USA 2: University of Iowa, Iowa City, USA 3: University of Oslo, Norway

Publication date: March 1, 2012

bpl/rssb/2012/00000074/00000002/art00002
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more