Balance and orthogonality in designs for mixed classification models

• 成果类型: Article
• 署名作者: Vanleeuwen, DM; Birkes, DS; Seely, JF
• 署名单位: New Mexico State University; Oregon State University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1999
• 页码: 1927-1947
• 关键词: Sufficient conditions

摘要

A classification model is easiest to analyze when it has a balanced design. Many of the nice features of balanced designs are retained by error-orthogonal designs, which were introduced in a recent paper by the authors. The present paper defines a kind of partially balanced design and shows that this pal tial balance is sufficient to ensure the error-orthogonality of a mixed classification model. Results are provided that make the partial balance condition easy to check. It is shown that, for a maximal-rank error-orthogonal design, the Type I sum of squares for a random effect coincides with the Type II sum of squares.