Mean squared error - a tool to evaluate the accuracy of parameter estimators in regression

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

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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

Item Type:Article
Keywords:estimator; mean squared error; regression
Journal:Journal of Quality Measurement and Analysis
ID Code:1853
Deposited By: Ms. Nor Ilya Othman
Deposited On:15 Jun 2011 04:18
Last Modified:15 Jun 2011 04:18

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