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A Semiparametric Approach to Dimension Reduction
| Content Provider | Scilit |
|---|---|
| Author | Ma, Yanyuan Zhu, Liping |
| Copyright Year | 2012 |
| Description | We provide a novel and completely different approach to dimension-reduction problems from the existing literature. We cast the dimension-reduction problem in a semiparametric estimation framework and derive estimating equations. Viewing this problem from the new angle allows us to derive a rich class of estimators, and obtain the classical dimension reduction techniques as special cases in this class. The semiparametric approach also reveals that in the inverse regression context while keeping the estimation structure intact, the common assumption of linearity and/or constant variance on the covariates can be removed at the cost of performing additional nonparametric regression. The semiparametric estimators without these common assumptions are illustrated through simulation studies and a real data example. This article has online supplementary material. |
| Related Links | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3698620/pdf |
| Ending Page | 179 |
| Page Count | 12 |
| Starting Page | 168 |
| ISSN | 01621459 |
| e-ISSN | 1537274X |
| DOI | 10.1080/01621459.2011.646925 |
| Journal | Journal of the American Statistical Association |
| Issue Number | 497 |
| Volume Number | 107 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2012-03-01 |
| Access Restriction | Open |
| Subject Keyword | Statistics and Probability Estimating Equations Nonparametric Regression Robustness Semiparametric Methods Sliced Inverse Regression |
| Content Type | Text |
| Subject | Statistics and Probability Statistics, Probability and Uncertainty |
