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.
| Original language | English |
|---|---|
| Pages (from-to) | 70-79 |
| Number of pages | 10 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 60 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2013 |
Bibliographical note
Funding Information:This work was supported by Basic Science Research Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Education, Science and Technology ( 2012-040077 ).
Keywords
- Bootstrap
- Categorical predictors
- Large p small n
- Seeded dimension reduction
- Survival regression