Robust techniques for linear regression with multicollinearity and outliers
The ordinary least squares (OLS) method is the most commonly used method in multiple linear regression model due to its optimal properties and ease of computation. Unfortunately, in the presence of multicollinearity and outlying observations in a data set, the OLS estimate is inefficient with inflat...
Saved in:
主要作者: | Mohammed, Mohammed Abdulhussein |
---|---|
格式: | Thesis |
语言: | English |
出版: |
2016
|
主题: | |
在线阅读: | http://psasir.upm.edu.my/id/eprint/58669/1/IPM%202016%201IR%20D.pdf |
标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
相似书籍
-
A robust ridge regression estimator in the presence of outliers and multicollinearity /
由: Marina Zahari
出版: (2001) -
Solution To The Multicollinearity Problem In Ridge Regression Model
由: Hanan Moh. B. Duzan -
Robust Estimation Methods and Robust Multicollinearity Diagnostics for Multiple Regression Model in the Presence of High Leverage Collinearity-Influential Observations
由: Bagheri, Arezoo
出版: (2011) -
Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers
由: Uraibi, Hassan S.
出版: (2016) -
A Robust Ridge Regression For Multicollinearity Problem In The Presence Of Outliers In The Data
由: Nur Aqilah Binti Ferdaos