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A Universal Selection Method in Linear Regression Models

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DOI: 10.4236/ojs.2012.22017    4,101 Downloads   7,926 Views   Citations

ABSTRACT

In this paper we consider a linear regression model with fixed design. A new rule for the selection of a relevant submodel is introduced on the basis of parameter tests. One particular feature of the rule is that subjective grading of the model complexity can be incorporated. We provide bounds for the mis-selection error. Simulations show that by using the proposed selection rule, the mis-selection error can be controlled uniformly.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

E. Liebscher, "A Universal Selection Method in Linear Regression Models," Open Journal of Statistics, Vol. 2 No. 2, 2012, pp. 153-162. doi: 10.4236/ojs.2012.22017.

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