Fig11.sav

NIAID Data Ecosystem2026-03-10 收录

信息度量分析

统计依赖性

数据链接：

https://figshare.com/articles/dataset/Fig11_sav/4967810 数据链接链接失效反馈

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资源简介：

Normalized Joint and Marginal Shannon information content and normalized mutual information content. Data corresponds to results of Figure 11 in paper.

创建时间：

2017-05-03

BET on Independence

统计依赖性

数据挖掘

We study the problem of nonparametric dependence detection. Many existing methods may suffer severe power loss due to nonuniform consistency, which we illustrate with a paradox. To avoid such power lo

DataCite Commons2024-02-14 更新360

Distance Metrics for Measuring Joint Dependence with Application to Causal Inference

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因果推断

Many statistical applications require the quantification of joint dependence among more than two random vectors. In this work, we generalize the notion of distance covariance to quantify joint depende

DataCite Commons2021-09-29 更新70

Randomization Tests for Equality in Dependence Structure

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经济金融依赖分析

We develop a new statistical procedure to test whether the dependence structure is identical between two groups. Rather than relying on a single index such as Pearson’s correlation coefficient or Kend

Taylor & Francis Group2020-05-18 更新50

Regression coefficients (β) for divergence measures.

统计模型解释

信息度量分析

Regression coefficients (β) for divergence measures.

NIAID Data Ecosystem80

Network Dependence Can Lead to Spurious Associations and Invalid Inference

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网络分析

Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a small number of communi

Taylor & Francis Group2021-09-29 更新60

Fig11.sav

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