A Universal Selection Method in Linear Regression Models

• 作者: Eckhard Liebscher
• 来源期刊: 统计学期刊(英文)
• 学科: 数学
• 关键词: LINEAR Regression MODEL SELECTION MULTIPLE TESTS
• 年，卷（期）: 2012,（2）
• 页码范围: 153-162
• 页数: 10页
• 分类号: O1

摘要

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.