Maximin clusters for near-replicate regression lack of fit tests

• 成果类型: Article
• 署名作者: Miller, FR; Neill, JW; Sherfey, BW
• 署名单位: Kansas State University; Kansas State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1998
• 页码: 1411-1433
• 关键词: models; designs

摘要

To assess the adequacy of a nonreplicated linear regression model, Christensen introduced the concepts of orthogonal between- and within-cluster lack of fit with corresponding optimal tests. However, the properties of these tests depend on the choice of near-replicate clusters. In this paper, a graph theoretic framework is presented to represent candidate clusterings. A clustering is then selected according to a proposed maximin power criterion from among the clusterings consistent with a specified graph on the predictor settings. Examples are given to illustrate the methodology.