A simplified approach to bias estimation for correlations

Q3 Mathematics

Epidemiologic Methods Pub Date : 2021-01-01 DOI:10.1515/em-2021-0015

X. Liu

引用次数: 1

Abstract

Abstract Objectives We introduce a simple and unified methodology to estimate the bias of Pearson correlation coefficients, partial correlation coefficients, and semi-partial correlation coefficients. Methods Our methodology features non-parametric bootstrapping and can accommodate small sample data without making any distributional assumptions. Results Two examples with R code are provided to illustrate the computation. Conclusions The computation strategy is easy to implement and remains the same, be it Pearson correlation or partial or semi-partial correlation.

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一种简化的相关性偏差估计方法

摘要目的介绍一种简单统一的方法来估计Pearson相关系数、偏相关系数和半偏相关系数的偏差。方法采用非参数自举方法，在不做任何分布假设的情况下适应小样本数据。结果给出了两个用R代码编写的算例。结论无论是Pearson相关还是偏、半偏相关，该计算策略易于实现且保持不变。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Epidemiologic Methods Mathematics-Applied Mathematics

CiteScore

2.10

自引率

0.00%

发文量

期刊介绍： Epidemiologic Methods (EM) seeks contributions comparable to those of the leading epidemiologic journals, but also invites papers that may be more technical or of greater length than what has traditionally been allowed by journals in epidemiology. Applications and examples with real data to illustrate methodology are strongly encouraged but not required. Topics. genetic epidemiology, infectious disease, pharmaco-epidemiology, ecologic studies, environmental exposures, screening, surveillance, social networks, comparative effectiveness, statistical modeling, causal inference, measurement error, study design, meta-analysis