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Advances in seeded dimension reduction: Bootstrap criteria and extensions

Title
Advances in seeded dimension reduction: Bootstrap criteria and extensions
Authors
Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2013
Journal Title
Computational Statistics and Data Analysis
ISSN
0167-9473JCR Link
Citation
vol. 60, no. 1, pp. 70 - 79
Indexed
SCIE; SCOPUS WOS scopus
Abstract
A seeded dimension reduction approach recently developed provides a paradigm to enable existing dimension reduction methods for the central subspace to be adapted to regressions with n<p. The approach is based on successive projection of a seed matrix on a subspace to contain the central subspace. In the article, we will suggest a bootstrap determination procedure to select a proper value for terminating the projections. Also, extensions of seeded dimension reduction are proposed to cover more various types of regressions with n<p such as a categorical predictor regression and survival regression. Then we apply the new development to analyze diffuse large-B-cell lymphoma data and leukemia data. Numerical studies are also presented. © 2012 Elsevier B.V. All rights reserved.
DOI
10.1016/j.csda.2012.10.003
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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