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Fast algorithm of Gram-Schmidt regression method

  • Huiwen Wang*
  • , Bang Xia
  • , Jie Meng
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)1259-1262+1268
JournalBeijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics
Volume39
Issue number9
StatePublished - Sep 2013

Keywords

  • Fast modeling
  • Gram-Schmidt orthogonal transformation
  • Redundant variables
  • Variable selection

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