序数回归
序数数据
有序逻辑
威尔科克森符号秩检验
顺序量表
逻辑回归
一般化
统计
数学
罗伊特
可能性
医学
连续变量
秩(图论)
优势比
计量经济学
曼惠特尼U检验
组合数学
数学分析
出处
期刊:Springer series in statistics
日期:2015-01-01
卷期号:: 311-325
被引量:135
标识
DOI:10.1007/978-3-319-19425-7_13
摘要
Many medical and epidemiologic studies incorporate an ordinal response variable. In some cases an ordinal response Y represents levels of a standard measurement scale such as severity of pain (none, mild, moderate, severe). In other cases, ordinal responses are constructed by specifying a hierarchy of separate endpoints. For example, clinicians may specify an ordering of the severity of several component events and assign patients to the worst event present from among none, heart attack, disabling stroke, and death. Still another use of ordinal response methods is the application of rank-based methods to continuous responses so as to obtain robust inferences. For example, the proportional odds model described later allows for a continuous Y and is really a generalization of the Wilcoxon–Mann–Whitney rank test. Thus the semiparametric proportional odds model is a direct competitor of ordinary linear models.
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