Properties of the ordinary least squares and stein-rule predictions in linear regression models with proxy variables

1993 ◽  
Vol 34 (1) ◽  
pp. 27-41 ◽  
Author(s):  
V. K. Srivastava ◽  
M. Dube
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Models ◽  
Ordinary Least Squares ◽  
Linear Regression Models ◽  
Proxy Variables

scholarly journals A Comparative Analysis on Some Estimators of Parameters of Linear Regression Models in Presence of Multicollinearity

2018 ◽  
pp. 1-8
Author(s):  
Warha, Abdulhamid Audu ◽  
Yusuf Abbakar Muhammad ◽  
Akeyede, Imam
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Models ◽  
Mean Squared Error ◽  
Least Squares Method ◽  
Ordinary Least Squares ◽  
Least Square ◽  
Linear Regression Models ◽  
Independent Variables ◽  
Different Levels

Linear regression is the measure of relationship between two or more variables known as dependent and independent variables. Classical least squares method for estimating regression models consist of minimising the sum of the squared residuals. Among the assumptions of Ordinary least squares method (OLS) is that there is no correlations (multicollinearity) between the independent variables. Violation of this assumptions arises most often in regression analysis and can lead to inefficiency of the least square method. This study, therefore, determined the efficient estimator between Least Absolute Deviation (LAD) and Weighted Least Square (WLS) in multiple linear regression models at different levels of multicollinearity in the explanatory variables. Simulation techniques were conducted using R Statistical software, to investigate the performance of the two estimators under violation of assumptions of lack of multicollinearity. Their performances were compared at different sample sizes. Finite properties of estimators’ criteria namely, mean absolute error, absolute bias and mean squared error were used for comparing the methods. The best estimator was selected based on minimum value of these criteria at a specified level of multicollinearity and sample size. The results showed that, LAD was the best at different levels of multicollinearity and was recommended as alternative to OLS under this condition. The performances of the two estimators decreased when the levels of multicollinearity was increased.

On Consistency of Estimators in Simple Linear Regression Models

2002 ◽  
Vol 53 (3-4) ◽  
pp. 261-264 ◽  
Author(s):  
Anindya Roy ◽  
Thomas I. Seidman
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Regression Models ◽  
Sufficient Condition ◽  
Simple Linear Regression ◽  
Linear Regression Models ◽  
Least Squares Estimators

We derive a property of real sequences which can be used to provide a natural sufficient condition for the consistency of the least squares estimators of slope and intercept for a simple linear regression models.

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