Abstract
For simultaneous dimension reduction and variable selection for general regression models, including the multi-index model as a special case, we propose a penalized minimum average variance estimation method, combining the ideas of minimum average variance estimation in dimension reduction and regularization in variable selection. The resulting estimator can be found in a computationally efficient manner. Under mild conditions, the new method can consistently select all relevant predictors and has the oracle property. Simulations and a data example demonstrate the effectiveness and efficiency of the proposed method.
Original language | English |
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Pages (from-to) | 543-569 |
Number of pages | 27 |
Journal | Statistica Sinica |
Volume | 23 |
Issue number | 2 |
DOIs | |
Publication status | Published - Apr 2013 |
Scopus Subject Areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
User-Defined Keywords
- Dimension reduction
- Minimum average variance estimation
- Oracle property
- Single-index model
- Sufficient dimension reduction
- Variable selection