[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
:: 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:   (2196 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]   (1268 Downloads)    
Type of Study: Research | Subject: General
Received: 2009/09/8 | Accepted: 2009/12/23 | Published: 2016/01/10
Send email to the article author

Add your comments about this article
Your username or Email:

CAPTCHA


XML   Persian Abstract   Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Shahini A, Sarmad M. Partial Least Squares Regression. مجله‌ی بررسی‌ها. 2010; 20 (2) :289-304
URL: http://ijoss.srtc.ac.ir/article-1-79-en.html


Volume 20, Issue 2 (3-2010) Back to browse issues page
مجله‌ی بررسی‌های آمار رسمی ایران (علمی - ترویجی) Ijoss Iranian Journal of Official Statistics Studies
Persian site map - English site map - Created in 0.04 seconds with 30 queries by YEKTAWEB 4256