Using Liu-Type Estimator to Combat Collinearity

By Kejian Liu. 2003

310 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/63f93ad68cd40db2fba4ce2d380cbf86ddee656b

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In Statistics and Probability

Citations in the corpus

The Restricted and Unrestricted Two-Parameter Estimators (2007, methodology): “15): as k approaches zero, ̂ k d approaches the OLS estimator and ̂r k d approaches the RLS estimator; as d approaches zero, ̂ k d approaches the RR estimator and ̂r k d approaches the restricted ridge estimator; as k approaches infinity, ̂ k d approaches the contraction estimator; and when k =”
A new biased estimator based on ridge estimation (2008, methodology): “Liu (2003) introduces a new estimator β̂k,d = (X ′X + kI)−1(X ′Y − dβ∗) (5)”
Ridge Estimation to the Restricted Linear Model (2007, methodology): “Some biased estimations, such as Shrinkage Estimation (SE), Principal Components Estimation (PCE), ridge estimation (RE), Partial Least Squares Estimation (PLSE), Liu type estimator (LE), Almost unbiased estimation (AUE), and so on (see Akdeniz and Kaciranlar, 1995 ; Liu, 2003 ; Radhakrishna Rao a”
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”
A new Liu-type estimator in linear regression model (2012, methodology): “To compare the five estimators, following Liu (2003) the explanatory variables and the observations on the dependent variable are generated by”