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Mitigating collinearity in linear regression models using ridge, surrogate and raised estimators
(Cogent OA, 2016)
Collinearity in the design matrix is a frequent problem in linear regression models, for example, with economic or medical data. Previous standard procedures to mitigate the effects of collinearity included ridge regression ...
Limitations of the least squares estimators; a teaching perspective
(Athens Institute for Education and Research, 2016)
The standard linear regression model can be written as Y = Xβ+ε with X a full rank n × p matrix and L(ε) = N(0, σ2In). The least squares estimator is = (X΄X)−1XY with variance-covariance matrix Coυ( ) = σ2(X΄X)−1, where ...