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Volume 34 Issue 11
Nov.  2008
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Journal of Beijing University of Aeronautics and Astronautics  > 2008  >  34(11): 1349-1352.
ZHOU X，ZHENG N S，DING R，et al. An improved inversion method of forest biomass based on satellite GNSS-R[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（8）：2619-2626 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0654
Citation: Wang Huiwen, Guo Lijuan. Polynomial regression modeling based on Gram-Schmidt process[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008, 34(11): 1349-1352. (in Chinese)
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Polynomial regression modeling based on Gram-Schmidt process

  • School of Economics and Management, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • Received Date: 13 Nov 2007
  • Publish Date: 30 Nov 2008
    Abstract
  • The polynomial regression model is a widely applied nonlinear regression method. Since the high correlation exists among independent variables in the polynomial regression model, it will induce excessive computational error to estimate coefficients with the ordinary least square regression. A method of polynomial regression modeling based on Gram-Schmidt process which can achieve the orthogonalization of the independent variables and overcome the adverse effects of multicollinearity to regression modeling was proposed, so as to apply ordinary least square to regression modeling effectively. The independent variables including notable explaining information can be selected effectively, at the same time redundant information is deleted. Simulation data analysis was adopted to test the effectiveness of the method.

     

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