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...
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Penerbit ukm
2008
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| Online Access: | http://journalarticle.ukm.my/1853/ http://journalarticle.ukm.my/1853/ |
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ukm-1853 |
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ukm-18532011-06-15T04:18:14Z http://journalarticle.ukm.my/1853/ Mean squared error - a tool to evaluate the accuracy of parameter estimators in regression Ng , Set Foong Low , Heng Chin Quah , Soon Hoe 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 Penerbit ukm 2008-07 Article PeerReviewed Ng , Set Foong and Low , Heng Chin and Quah , Soon Hoe (2008) Mean squared error - a tool to evaluate the accuracy of parameter estimators in regression. Journal of Quality Measurement and Analysis, 4 (1). pp. 71-80. ISSN 1823-5670 http://www.ukm.my/~ppsmfst/jqma/index.html |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Kebangasaan Malaysia |
| building |
UKM Institutional Repository |
| collection |
Online Access |
| description |
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 |
| format |
Article |
| author |
Ng , Set Foong Low , Heng Chin Quah , Soon Hoe |
| spellingShingle |
Ng , Set Foong Low , Heng Chin Quah , Soon Hoe Mean squared error - a tool to evaluate the accuracy of parameter estimators in regression |
| author_facet |
Ng , Set Foong Low , Heng Chin Quah , Soon Hoe |
| author_sort |
Ng , Set Foong |
| title |
Mean squared error - a tool to evaluate the accuracy of
parameter estimators in regression
|
| title_short |
Mean squared error - a tool to evaluate the accuracy of
parameter estimators in regression
|
| title_full |
Mean squared error - a tool to evaluate the accuracy of
parameter estimators in regression
|
| title_fullStr |
Mean squared error - a tool to evaluate the accuracy of
parameter estimators in regression
|
| title_full_unstemmed |
Mean squared error - a tool to evaluate the accuracy of
parameter estimators in regression
|
| title_sort |
mean squared error - a tool to evaluate the accuracy of
parameter estimators in regression |
| publisher |
Penerbit ukm |
| publishDate |
2008 |
| url |
http://journalarticle.ukm.my/1853/ http://journalarticle.ukm.my/1853/ |
| first_indexed |
2023-09-18T19:34:30Z |
| last_indexed |
2023-09-18T19:34:30Z |
| _version_ |
1777405180949561344 |