J. Shao, “An Asymptotic Theory for Linear Model Selection,” Statistica Sinica, Vol. 7, 1997, pp. 221-264.
has been cited by the following article:
TITLE: A Universal Selection Method in Linear Regression Models
AUTHORS: Eckhard Liebscher
KEYWORDS: Linear Regression; Model Selection; Multiple Tests
JOURNAL NAME: Open Journal of Statistics, Vol.2 No.2, April 23, 2012
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.