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A new stochastic mixed ridge estimator in linear regression model. (English) Zbl 1247.62179
Summary: We are concerned with parameter estimation in linear regression models with additional stochastic linear restrictions. To overcome the multicollinearity problem, a new stochastic mixed ridge estimator is proposed and its efficiency is discussed. Necessary and sufficient conditions for the superiority of the stochastic mixed ridge estimator over the ridge estimator and the mixed estimator in the mean squared error matrix sense are derived for the two cases in which the parametric restrictions are correct or are not correct. Finally, a numerical example is also given to show the theoretical results.

MSC:
62J07 Ridge regression; shrinkage estimators (Lasso)
62J05 Linear regression; mixed models
62F30 Parametric inference under constraints
62H12 Estimation in multivariate analysis
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