*Biased Estimation

  • Ashish Sen
  • Muni Srivastava
Part of the Springer Texts in Statistics book series (STS)


One purpose of variable selection is to reduce multicollinearity, although, as we noted in Section 11.2, reducing the number of independent variables can lead to bias. Obviously, the general principle is that it might be preferable to trade off a small amount of bias in order to substantially reduce the variances of the estimates of β. There are several other methods of estimation which are also based on trading off bias for variance. This chapter describes three of these: principal component regression, ridge regression and the shrinkage estimator.


Unbiased Estimator Ridge Regression Principal Component Regression Error Matrix Shrinkage Estimator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York Inc. 1990

Authors and Affiliations

  • Ashish Sen
    • 1
  • Muni Srivastava
    • 2
  1. 1.College of Architecture, Art, and Urban Planning School of Urban Planning and PolicyThe University of IllinoisChicagoUSA
  2. 2.Department of StatisticsUniversity of TorontoTorontoCanada

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