Detection of influential observations in principle component regression
Multicollinearity that may exist among explanatory variables in a regression model can make the regression coefficients insignificant and difficult to interpret. Principal component regression (PCR) is an effective way for solving multicollinearity in regression analysis. The existence of multicolli...
Main Author: | Mokhtar Abdullah |
---|---|
Format: | Article |
Published: |
Universiti Kebangsaan Malaysia
1996
|
Similar Items
-
Detection of outliers and influential observations in binary logistic regression: An empirical study.
by: Sarkar, S.K., et al.
Published: (2011) -
Fast improvised influential distance for the identification of influential observations in multiple linear regression
by: Midi, Habshah, et al.
Published: (2021) -
Detection of influential observations in spatial regression model based on outliers and bad leverage classification
by: Mohammed Baba, Ali, et al.
Published: (2021) -
Robust Estimation Methods and Robust Multicollinearity Diagnostics for Multiple Regression Model in the Presence of High Leverage Collinearity-Influential Observations
by: Bagheri, Arezoo
Published: (2011) -
Asymptotic covariance and detection of influential observations in a linear functional relationship model for circular data with application to the measurements of wind directions
by: Hussin, A.G., et al.
Published: (2010)