A new stochastic mixed ridge estimator in linear regression model Note

First Online: 23 September 2008 Received: 21 April 2008 Revised: 15 August 2008 DOI :
10.1007/s00362-008-0169-5

Cite this article as: Li, Y. & Yang, H. Stat Papers (2010) 51: 315. doi:10.1007/s00362-008-0169-5 Abstract This paper is concerned with the parameter estimation in linear regression model 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 and are not correct. Finally, a numerical example is also given to show the theoretical results.

Keywords Ordinary ridge estimator Ordinary mixed estimator Stochastic mixed ridge estimator Mean squared error matrix

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