Mean squared error - a tool to evaluate the accuracy of parameter estimators in regression
Linear regression model is frequently used to describe the relationship between a dependent variable and several independent variables. Thus, regression analysis is very useful in many application areas. In a linear regression model, there are unknown parameters to be estimated. The least squares...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Penerbit ukm
2008
|
| Online Access: | http://journalarticle.ukm.my/1853/ http://journalarticle.ukm.my/1853/ |
| Summary: | Linear regression model is frequently used to describe the relationship between a dependent
variable and several independent variables. Thus, regression analysis is very useful in many
application areas. In a linear regression model, there are unknown parameters to be estimated.
The least squares estimator is most commonly used to estimate the unknown parameters. In
addition, several other estimators are also proposed as alternatives to least squares estimator.
A tool to evaluate the performance of these estimators is necessary. In this paper, mean
squared error is shown as a tool to compare the accuracy of two estimators. An estimator with
higher accuracy would be considered as a better estimator. As an example, two parameter
estimators are compared using mean squared error as comparison tool. The parameter
estimators are the Liu Estimator and the special case of Liu-type estimator |
|---|