Show simple item record  

dc.contributor.authorJung, Yoonsuhen_NZ
dc.date.accessioned2016-03-08T20:13:19Z
dc.date.available2016-02-08en_NZ
dc.date.available2016-03-08T20:13:19Z
dc.date.issued2016-02-08en_NZ
dc.identifier.citationJung, Y. (2016). Efficient tuning parameter selection by cross-validated score in high dimensional models. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 10(1), 19–25.en
dc.identifier.urihttps://hdl.handle.net/10289/9980
dc.description.abstractAs DNA microarray data contain relatively small sample size compared to the number of genes, high dimensional models are often employed. In high dimensional models, the selection of tuning parameter (or, penalty parameter) is often one of the crucial parts of the modeling. Cross-validation is one of the most common methods for the tuning parameter selection, which selects a parameter value with the smallest cross-validated score. However, selecting a single value as an ‘optimal’ value for the parameter can be very unstable due to the sampling variation since the sample sizes of microarray data are often small. Our approach is to choose multiple candidates of tuning parameter first, then average the candidates with different weights depending on their performance. The additional step of estimating the weights and averaging the candidates rarely increase the computational cost, while it can considerably improve the traditional cross-validation. We show that the selected value from the suggested methods often lead to stable parameter selection as well as improved detection of significant genetic variables compared to the tradition cross-validation via real data and simulated data sets.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherWorld Academy of Science, Engineering and technologyen_NZ
dc.relation.urihttp://waset.org/Publication/efficient-tuning-parameter-selection-by-cross-validated-score-in-high-dimensional-models/10003720en_NZ
dc.rightsThis article is published in the International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering
dc.titleEfficient tuning parameter selection by cross-validated score in high dimensional modelsen_NZ
dc.typeJournal Article
dc.relation.isPartOfInternational Journal of Mathematical, Computational, Physical, Electrical and Computer Engineeringen_NZ
pubs.begin-page19
pubs.elements-id137348
pubs.end-page25
pubs.issue1en_NZ
pubs.publication-statusPublisheden_NZ
pubs.publisher-urlhttp://waset.org/publications/10003720en_NZ
pubs.volume10en_NZ


Files in this item

This item appears in the following Collection(s)

Show simple item record