The Comparison of Prediction Ability for Partial Least Squares, Principal Components Regression, Ridge Regression, and Forward Variable Selection.
碩士 === 國立中興大學 === 農藝學系 === 91 === It is possible to deal with several explanatory variables simultaneously in data analysis. The simplest way is to use multiple linear regression (MLR) when more than one variable is employed. However, collinearities among variables somtimes make regression estimates...
Main Authors: | Tzun-wen Shaw, 邵遵文 |
---|---|
Other Authors: | Bo-Jein Kuo |
Format: | Others |
Language: | zh-TW |
Published: |
2003
|
Online Access: | http://ndltd.ncl.edu.tw/handle/46061049874078465636 |
Similar Items
-
Comparison of Least Squares, Ridge Regression and Principal Component Approaches in the Presence of Multicollinearity in Regression Analysis
by: Soner Çankaya, et al.
Published: (2019-08-01) -
Quantification of prediction uncertainty for principal components regression and partial least squares regression
by: Zhang, Y.
Published: (2014) -
A Comparison Study of Principle Component Regression, Partial Least Square Regression and Ridge Regression with Application to FTIR Data
by: Li, Ying
Published: (2010) -
The pls Package: Principal Component and Partial Least Squares Regression in R
by: Bjørn-Helge Mevik
Published: (2007-01-01) -
The Study on the Partial Least Squares Regression, Principal Component Regression, and Neural Networks to Analyze the Near-Infrared Spectrum Data
by: Yung-Heng Hsu, et al.
Published: (2003)