Dimension Reduction for Classification with Gene Expression Microarray Data
Dai Jian J,
Lieu Linh and
Additional contact information
Dai Jian J: University of California, Davis
Lieu Linh: University of California, Los Angeles
Rocke David: University of California, Davis
Statistical Applications in Genetics and Molecular Biology, 2006, vol. 5, issue 1, 1-21
An important application of gene expression microarray data is classification of biological samples or prediction of clinical and other outcomes. One necessary part of multivariate statistical analysis in such applications is dimension reduction. This paper provides a comparison study of three dimension reduction techniques, namely partial least squares (PLS), sliced inverse regression (SIR) and principal component analysis (PCA), and evaluates the relative performance of classification procedures incorporating those methods. A five-step assessment procedure is designed for the purpose. Predictive accuracy and computational efficiency of the methods are examined. Two gene expression data sets for tumor classification are used in the study.
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
For access to full text, subscription to the journal or payment for the individual article is required.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:5:y:2006:i:1:n:6
Ordering information: This journal article can be ordered from
Access Statistics for this article
Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf
More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().