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引用次数: 0
摘要
在这篇文章中,我为克里彭多夫的阿尔法一致系数推荐了一个更好的点估计器,并开发了一个杰克刀方差估计器,它能比习惯的自举程序或替代自举程序带来更好的区间估计。在开发出新方法后,我分析了克里彭多夫之前分析过的名义数据,以及两个实验观察数据集:(1) 来自先天性膈疝成像研究的序数数据,以及 (2) 美国环境保护局提供的宾夕法尼亚州费城地区空气污染数据。后两个应用都很新颖。现在,我的开源 R 软件包 krippendorffsalpha 的 2.0 版本支持所提出的方法,该软件包支持常见的和用户定义的距离函数,并能容纳任意数量的单位、任意数量的编码器和缺失。区间计算可以并行化。
Toward improved inference for Krippendorff’s Alpha agreement coefficient
In this article I recommend a better point estimator for Krippendorff’s Alpha agreement coefficient, and develop a jackknife variance estimator that leads to much better interval estimation than does the customary bootstrap procedure or an alternative bootstrap procedure. Having developed the new methodology, I analyze nominal data previously analyzed by Krippendorff, and two experimentally observed datasets: (1) ordinal data from an imaging study of congenital diaphragmatic hernia, and (2) United States Environmental Protection Agency air pollution data for the Philadelphia, Pennsylvania area. The latter two applications are novel. The proposed methodology is now supported in version 2.0 of my open source R package, krippendorffsalpha, which supports common and user-defined distance functions, and can accommodate any number of units, any number of coders, and missingness. Interval computation can be parallelized.
期刊介绍:
The Journal of Statistical Planning and Inference offers itself as a multifaceted and all-inclusive bridge between classical aspects of statistics and probability, and the emerging interdisciplinary aspects that have a potential of revolutionizing the subject. While we maintain our traditional strength in statistical inference, design, classical probability, and large sample methods, we also have a far more inclusive and broadened scope to keep up with the new problems that confront us as statisticians, mathematicians, and scientists.
We publish high quality articles in all branches of statistics, probability, discrete mathematics, machine learning, and bioinformatics. We also especially welcome well written and up to date review articles on fundamental themes of statistics, probability, machine learning, and general biostatistics. Thoughtful letters to the editors, interesting problems in need of a solution, and short notes carrying an element of elegance or beauty are equally welcome.