Loading...
Please wait, while we are loading the content...
Similar Documents
Groupwise Dimension Reduction via Envelope Method
| Content Provider | Scilit |
|---|---|
| Author | Guo, Zifang Li, Lexin Lu, Wenbin Li, Bing |
| Copyright Year | 2015 |
| Description | Journal: Journal of the American Statistical Association The family of sufficient dimension reduction (SDR) methods that produce informative combinations of predictors, or indices, are particularly useful for high dimensional regression analysis. In many such analyses, it becomes increasingly common that there is available a priori subject knowledge of the predictors; e.g., they belong to different groups. While many recent SDR proposals have greatly expanded the scope of the methods' applicability, how to effectively incorporate the prior predictor structure information remains a challenge. In this article, we aim at dimension reduction that recovers full regression information while preserving the predictor group structure. Built upon a new concept of the direct sum envelope, we introduce a systematic way to incorporate the group information in most existing SDR estimators. As a result, the reduction outcomes are much easier to interpret. Moreover, the envelope method provides a principled way to build a variety of prior structures into dimension reduction analysis. Both simulations and real data analysis demonstrate the competent numerical performance of the new method. |
| Related Links | http://europepmc.org/articles/pmc4787236?pdf=render https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4787236/pdf |
| Ending Page | 1527 |
| Page Count | 13 |
| Starting Page | 1515 |
| ISSN | 01621459 |
| e-ISSN | 1537274X |
| DOI | 10.1080/01621459.2014.970687 |
| Journal | Journal of the American Statistical Association |
| Issue Number | 512 |
| Volume Number | 110 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2015-10-02 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of the American Statistical Association Statistics and Probability Central Subspace Direct Sum Envelope Groupwise Dimension Reduction Multiple-index Models Sliced Inverse Regression |
| Content Type | Text |
| Resource Type | Article |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |
