On Biased Estimation in Linear Models

By Zhi-Fu Wang, Xian-Wei Yu, Jing Zhang, Na Li, Wei Zhao, Li Li. International Conference on Machine Learning and Computing, 2006

76 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

A Modified New Two-Parameter Estimator in a Linear Regression Model (2019, methodology): “Mayer and Willke [4] defined the contraction estimator 􏽢 β(ρ) � (1 + ρ)􏽢 β, ρ> 0.”
A New Two-Parameter Estimator in Linear Regression (2010, methodology): “To overcome this problem, many new biased estimators have been proposed, such as Stein estimator (Stein, 1956), principal components estimator (Massy, 1965), RR estimator (Hoerl and Kennard, 1970), contraction estimator (Mayer and Willke, 1973), and modified ridge regression (MRR) estimator (Swindel”
The Restricted and Unrestricted Two-Parameter Estimators (2007, background): “To overcome multicollinearity, Mayer and Willke (1973) defined the contraction estimator”
Training-based MIMO channel estimation: a study of estimator tradeoffs and optimal training signals (2006, background): “Scaled LS estimators that further reduce the MSE by allowing for a bias is a common approach that has emerged in statistics [23], [24].”
On some beta ridge regression estimators: method, simulation and application (2021, methodology): “However, many biased estimators have been introduced to combat multicollinearity in linear regression, such as Stein estimator [4], principal component estimator [5], ridge regression estimator [6], improved ridge estimators [7] contraction estimator [8], modified ridge regression estimator [9], Liu”