Abstract
The ridge regression approach can also be applied to deal with multicollinearity in the seemingly unrelated regression equations (SURE) models. However, to get the ridge-type estimators of the coefficients in a SURE model, choosing the ridge parameter has always been an important and challenging task. Herein, to cope with this issue, an optimal choice of the ridge parameter is proposed based on the generalized cross-validation (GCV) criterion. Moreover, some existing estimators of this parameter, in the context of the ordinary ridge regression models, are extended to be used in the SURE models. All these estimators, including the proposed and extended ones, and several state-of-the-art alternatives (altogether 36 different estimators) are compared with each other in terms of the GCV criterion. Lastly, an application of the methodology is given on chronic renal failure effect data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
