Biased Estimation in Regression: An Evaluation Using Mean Squared Error
作者:
RichardF. Gunst,
RobertL. Mason,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1977)
卷期:
Volume 72,
issue 359
页码: 616-628
ISSN:0162-1459
年代: 1977
DOI:10.1080/01621459.1977.10480625
出版商: Taylor & Francis Group
关键词: Regression analysis;Biased estimation;Multicollinearity
数据来源: Taylor
摘要:
A mean squared error criterion is used to compare five estimators of the coefficients in a linear regression model: least squares, principal components, ridge regression, latent root, and a shrunken estimator. Each of the biased estimators is shown to offer improvement in mean squared error over least squares for a wide range of choices of the parameters of the model. The results of a simulation involving all five estimators indicate that the principal components and latent root estimators perform best overall, but the ridge regression estimator has the potential of a smaller mean squared error than either of these.
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