论文检索

含缺失数据的不平衡设计中的 Friedman 型统计量与一致多重比较AI 解读

A generalization of the Friedman test using the marginal likelihood principle (Kalbfleisch and Prentice 1973) is presented and its asymptotic power given. It allows use of a variety of score functions and handles ties and unbalanced designs. Some well-known statistics [including the tests of Kruskal and Wallis (1952), Prentice (1979), and Rinaman (1983)] are proven to be special cases, whereas others (e.g., Klotz 1980; Groggel and Skillings 1986; Rai 1987; Skillings and Mack…

含缺失数据的不平衡设计中的Friedman型统计量与一致多重比较AI 解读

A generalization of the Friedman test using the marginal likelihood principle (Kalbfleisch and Prentice 1973) is presented and its asymptotic power given. It allows use of a variety of score functions and handles ties and unbalanced designs. Some well-known statistics [including the tests of Kruskal and Wallis (1952), Prentice (1979), and Rinaman (1983)] are proven to be special cases, whereas others (e.g., Klotz 1980; Groggel and Skillings 1986; Rai 1987; Skillings and Mack…