基于预期一致指数无偏估计器的各种卡帕系数估计器

IF 1.4 4区 计算机科学 Q2 STATISTICS & PROBABILITY
A. Martín Andrés, M. Álvarez Hernández
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引用次数: 0

摘要

为了测量在 K 个类别中独立对 n 个受试者进行分类的 R 个观察者之间的一致程度,通常会使用各种卡帕类型的系数。当 R = 2 时,通常使用 Cohen' kappa、Scott's pi、Gwet's AC1/2 和 Krippendorf's alpha 系数(加权或不加权)。当 R > 2 时,通常使用基于上述系数的成对版本;顺序与上述相同:休伯特卡帕、弗莱斯卡帕、Gwet AC1/2 和 Krippendorf α。然而,所有这些统计都是基于有偏差的预期一致指数估计值,因为它们通过样本估计值的乘积来估计两个人口比例的乘积。本文的目的有三。首先,提供基于预期一致指数无偏估计值的统计量,并根据原始统计量的方差确定其方差。第二,对一些测量方法进行成对扩展。第三,证明科恩卡帕系数和休伯特卡帕系数的新旧估计值与众所周知的一致性和类内相关系数估计值相匹配,如果前者是通过假设二次加权来定义的话。文章表明,新估计值总是大于或等于经典估计值,除了 Gwet 的情况正好相反,不过这些差异只与小样本量(例如 n≤ 30)有关。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimators of various kappa coefficients based on the unbiased estimator of the expected index of agreements

To measure the degree of agreement between R observers who independently classify n subjects within K categories, various kappa-type coefficients are often used. When R = 2, it is common to use the Cohen' kappa, Scott's pi, Gwet’s AC1/2, and Krippendorf's alpha coefficients (weighted or not). When R > 2, some pairwise version based on the aforementioned coefficients is normally used; with the same order as above: Hubert's kappa, Fleiss's kappa, Gwet's AC1/2, and Krippendorf's alpha. However, all these statistics are based on biased estimators of the expected index of agreements, since they estimate the product of two population proportions through the product of their sample estimators. The aims of this article are three. First, to provide statistics based on unbiased estimators of the expected index of agreements and determine their variance based on the variance of the original statistic. Second, to make pairwise extensions of some measures. And third, to show that the old and new estimators of the Cohen’s kappa and Hubert’s kappa coefficients match the well-known estimators of concordance and intraclass correlation coefficients, if the former are defined by assuming quadratic weights. The article shows that the new estimators are always greater than or equal the classic ones, except for the case of Gwet where it is the other way around, although these differences are only relevant with small sample sizes (e.g. n ≤ 30).

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来源期刊
CiteScore
3.40
自引率
6.20%
发文量
45
审稿时长
>12 weeks
期刊介绍: The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.
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