Advanced Search

Indexed by SCI、CA、РЖ、PA、CSA、ZR、etc .

Volume 22 Issue 5
Oct.  2011
Turn off MathJax
Article Contents

Lin Li*. Quantifying TiO2 Abundance of Lunar Soils: Partial Least Squares and Stepwise Multiple Regression Analysis for Determining Causal Effect. Journal of Earth Science, 2011, 22(5). doi: 10.1007/s12583-011-0206-5
Citation: Lin Li*. Quantifying TiO2 Abundance of Lunar Soils: Partial Least Squares and Stepwise Multiple Regression Analysis for Determining Causal Effect. Journal of Earth Science, 2011, 22(5). doi: 10.1007/s12583-011-0206-5

Quantifying TiO2 Abundance of Lunar Soils: Partial Least Squares and Stepwise Multiple Regression Analysis for Determining Causal Effect

doi: 10.1007/s12583-011-0206-5
  • Received Date: 2018-07-15
  • Rev Recd Date: 2018-07-15
  • Publish Date: 2018-07-15
  • Partial least squares (PLS) regression was applied to the Lunar Soil Characterization Consortium (LSCC) dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the low-Ti, high-Ti, total mare soils, total highland, Apollo 16, and Apollo 14 soils to investigate the effects of interfering minerals and nonlinearity on the PLS performance. The PLS weight loading vectors were analyzed through stepwise multiple regression analysis (SMRA) to identify mineral species driving and interfering the PLS performance. PLS exhibits high performance for esti-mating TiO2 for the LSCC low-Ti and high-Ti mare samples and both groups analyzed together...
  • 加载中
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(949) PDF downloads(19) Cited by()

Related
Proportional views

Quantifying TiO2 Abundance of Lunar Soils: Partial Least Squares and Stepwise Multiple Regression Analysis for Determining Causal Effect

doi: 10.1007/s12583-011-0206-5

Abstract: Partial least squares (PLS) regression was applied to the Lunar Soil Characterization Consortium (LSCC) dataset for spectral estimation of TiO2. The LSCC dataset was split into a number of subsets including the low-Ti, high-Ti, total mare soils, total highland, Apollo 16, and Apollo 14 soils to investigate the effects of interfering minerals and nonlinearity on the PLS performance. The PLS weight loading vectors were analyzed through stepwise multiple regression analysis (SMRA) to identify mineral species driving and interfering the PLS performance. PLS exhibits high performance for esti-mating TiO2 for the LSCC low-Ti and high-Ti mare samples and both groups analyzed together...

Lin Li*. Quantifying TiO2 Abundance of Lunar Soils: Partial Least Squares and Stepwise Multiple Regression Analysis for Determining Causal Effect. Journal of Earth Science, 2011, 22(5). doi: 10.1007/s12583-011-0206-5
Citation: Lin Li*. Quantifying TiO2 Abundance of Lunar Soils: Partial Least Squares and Stepwise Multiple Regression Analysis for Determining Causal Effect. Journal of Earth Science, 2011, 22(5). doi: 10.1007/s12583-011-0206-5

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return