MAXIMUM-LIKELIHOOD-ESTIMATION OF AGREEMENT IN THE CONSTANT PREDICTIVE PROBABILITY MODEL, AND ITS RELATION TO COHEN KAPPA
MAXIMUM-LIKELIHOOD-ESTIMATION OF AGREEMENT IN THE CONSTANT PREDICTIVE PROBABILITY MODEL, AND ITS RELATION TO COHEN KAPPA
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DOI：
10.2307/2531434
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发表时间：
1990-06-01
期刊：
影响因子：
1.9
通讯作者：
AICKIN, M
中科院分区：
文献类型：
--
作者：
AICKIN, M
阿尔法。一致性参数被定义为由两个分类器以相同的原因分类的一组项目的比例，其余的项目被随机分类。用极大似然法证明了相应的常值预测概率模型的参数是可估计的，仿真研究表明，渐近结果对有限样本是适用的。新的估计量倾向于大于科恩的S卡帕，除了在均匀边际的情况下。应用于癌症登记中癌症危险项目的有效性。
The .alpha. agreement parameter is defined as the proportion of a population of items that are classified identically "for cause" by two classifiers, the remaining items being classified at random. The parameters of the corresponding constant predictive probability model are shown to be estimable by the method of maximum likelihood, and a simulation study indicates applicability of the asymptotic results to finite samples. The new estimator tends to be larger than Cohen''s kappa, except in the case of uniform margins. An application is made to the validity of cancer risk items included in a cancer registry.