Abstract
A new multiple linear regression method was proposed which can screen the variables fast. In the modeling process, not only can it screen variables containing best information to explain the dependent variable, but also distinguish and test redundant variables in the model based on Gram-Schmidt orthogonal transformation, so as to timely strike out all the redundant information in quantity. The simulation analysis shows that compared to classic stepwise regression this new method has a higher arithmetic speed and the modeling process is briefer and more efficient, when the variables appear in a large quantity and have a pretty serious server multicollinearity at the same time.
| Original language | English |
|---|---|
| Pages (from-to) | 1259-1262+1268 |
| Journal | Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics |
| Volume | 39 |
| Issue number | 9 |
| State | Published - Sep 2013 |
Keywords
- Fast modeling
- Gram-Schmidt orthogonal transformation
- Redundant variables
- Variable selection
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