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关于在分层数据中应用评分的警示说明。

A cautionary note on applying scores in stratified data.

作者信息

Podgor M J, Gastwirth J L

机构信息

National Eye Institute, Division of Biometry and Epidemiology, National Institutes of Health, Bethesda, Maryland 20892.

出版信息

Biometrics. 1994 Dec;50(4):1215-8.

PMID:7787004
Abstract

When rank tests are used to analyze stratified data, three methods for assigning scores to the observations have been proposed: (S) independently within each stratum (see Lehmann, 1975, Nonparametrics: Statistical Methods Based on Ranks; San Francisco: Holden-Day); (A) after aligning the observations within each stratum and then pooling the aligned observations (Hodges and Lehmann, 1962, Annals of Mathematical Statistics 33, 482-497); and (P) after pooling the observations across all strata (that is, without alignment) (Mantel, 1963, Journal of the American Statistical Association 58, 690-700; Mantel and Ciminera, 1979, Cancer Research 39, 4308-4315). Test statistics are formed for each method by combining the stratum-specific linear rank tests using the assigned scores. We show that method P is sensitive to the score function used in the case of two moderately sized strata. In general, we recommend methods S and A for use with moderate to large-sized strata.

摘要

当使用秩检验来分析分层数据时,已提出三种为观测值赋值的方法:(S) 在每个分层内独立赋值(见Lehmann,1975年,《非参数统计:基于秩的统计方法》;旧金山:霍尔登 - 戴公司);(A) 在对每个分层内的观测值进行对齐后,再将对齐后的观测值合并(霍奇斯和莱曼,1962年,《数理统计年鉴》33卷,482 - 497页);以及(P) 在将所有分层的观测值合并后(即不进行对齐)(曼特尔,1963年,《美国统计协会杂志》58卷,690 - 700页;曼特尔和奇米内拉,1979年,《癌症研究》39卷,4308 - 4315页)。通过使用赋值分数将特定分层的线性秩检验组合起来,为每种方法形成检验统计量。我们表明,在有两个中等规模分层的情况下,方法P对所使用的分数函数敏感。一般来说,对于中等规模到大规模的分层,我们推荐使用方法S和A。

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