Abstract
Research on modelprocedure selection has focused on selecting a single model globally. In many applications, especially for high-dimensional or complex data, however, the relative performance of the candidate procedures typically depends on the location, and the globally best procedure can often be improved when selection of a model is allowed to depend on location. We consider localized model selection methods and derive their theoretical properties.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 472-492 |
| Number of pages | 21 |
| Journal | Econometric Theory |
| Volume | 24 |
| Issue number | 2 |
| DOIs | |
| State | Published - Apr 2008 |
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Cite this
Yang, Y. (2008). Localized model selection for regression. Econometric Theory, 24(2), 472-492. https://doi.org/10.1017/S0266466608080195