贝叶斯自举相关系数

IF 1.3
Josue E. Rodriguez, Donald R Williams
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引用次数: 1

摘要

我们提出贝叶斯自举(BB)作为一种通用的、简单的、可访问的方法,用于从社会行为科学中常用的各种相关系数的后验分布中抽样。在一系列示例中,我们演示了如何使用BB来估计Pearson’s、Spearman’s、高斯秩、Kendall’s τ和多周期相关性。我们还描述了一种基于实际等价区域的方法来评估估计相关性之间的差异和零关联。此外,我们已经在R包BBcor (https://cran.r-project.org/web/packages/BBcor/index.html)中实现了该方法。应用实例说明了所提方法的示例代码和主要优点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Bootstrapped Correlation Coefficients
We propose the Bayesian bootstrap (BB) as a generic, simple, and accessible method for sampling from the posterior distribution of various correlation coefficients that are commonly used in the social-behavioral sciences. In a series of examples, we demonstrate how the BB can be used to estimate Pearson’s, Spearman’s, Gaussian rank, Kendall’s τ , and polychoric correlations. We also describe an approach based on a region of practical equivalence to evaluate differences and null associations among the estimated correlations. In addition, we have implemented the methodology in the R package BBcor (https://cran.r-project.org/web/packages/BBcor/index.html). Example code and key advantages of the proposed methods are illustrated in an applied example.
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