:: Volume 20, Issue 2 (3-2010) ::
مجله‌ی بررسی‌ها 2010, 20(2): 289-304 Back to browse issues page
Partial Least Squares Regression
Amir Shahini *, Majid Sarmad
Abstract:   (3332 Views)

 In different researches, we deal with problems that need to predict the behavior of a response variable by a set of explanatory variables. Multiple regression is one of the statistical methods that is widely used in these type of situations. When there is a linear relation between explanatory variables we will faced with multi-collinearity problem that causes ordinary least squares estimators of regression coefficients not to be robust. Partial least squares regression is a multivariate method that used when there is high collinearity between explanatory variables. In this paper, this method is introduced.

Keywords: Cross validation, prediction, multicollinearity, component, PLS
Full-Text [PDF 404 kb]   (2904 Downloads)    
Type of Study: Research | Subject: General
Received: 2009/09/8 | Accepted: 2009/12/23 | Published: 2016/01/10


XML   Persian Abstract   Print



Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 20, Issue 2 (3-2010) Back to browse issues page