Measures of Dependence

2009 ◽  
pp. 141-177 ◽  
Author(s):  
N. Balakrishna ◽  
Chin Diew Lai
Keyword(s):  
Measures Of Dependence

scholarly journals Equivalent measures of dependence

1983 ◽  
Vol 13 (1) ◽  
pp. 167-176 ◽  
Author(s):  
Richard C Bradley
Keyword(s):  
Measures Of Dependence

Symmetric rank covariances: a generalized framework for nonparametric measures of dependence

Biometrika ◽  
2018 ◽  
Vol 105 (3) ◽  
pp. 547-562 ◽  
Author(s):  
L Weihs ◽  
M Drton ◽  
N Meinshausen
Keyword(s):  
Measures Of Dependence ◽  
Generalized Framework

Perceptual and Interpersonal Measures of Field Dependence

1965 ◽  
Vol 20 (1) ◽  
pp. 79-86 ◽  
Author(s):  
Joe B. Alexander ◽  
Howard E. Gudeman
Keyword(s):  
Field Dependence ◽  
Psychiatric Patients ◽  
Specific Relationship ◽  
Measures Of Dependence ◽  
Dependence Measures ◽  
Relationship Of ◽  
The Relationship ◽  
Larger Sample ◽  
Rod And Frame Test ◽  
Rod And Frame

This study was concerned with the relationship between perceptual and interpersonal measures of dependence for a sample of 60 male Ss. Four groups of alcoholics, one group of hospitalized psychiatric patients, and a group of normals were compared on the Rod and Frame Test and three laboratory interpersonal tasks to evaluate the hypothesis that perceptual and interpersonal dependence measures are significantly related. The results only partially confirmed the hypothesis. The over-all correlation was significant, as was the over-all correlation for four groups of alcoholics. Only two of the six subgroup correlations, however, were significant. These results suggest the need for further study, using larger sample sizes, to determine the specific relationship of the two variables.

scholarly journals Two Measures of Dependence

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 778 ◽  
Author(s):  
Amos Lapidoth ◽  
Christoph Pfister
Keyword(s):  
Mutual Information ◽  
Data Processing ◽  
Optimal Error ◽  
Error Exponents ◽  
Rényi Divergence ◽  
Measures Of Dependence ◽  
Data Processing Inequality ◽  
Two Measures ◽  
Task Encoding ◽  
Dependence Measures

Two families of dependence measures between random variables are introduced. They are based on the Rényi divergence of order α and the relative α -entropy, respectively, and both dependence measures reduce to Shannon’s mutual information when their order α is one. The first measure shares many properties with the mutual information, including the data-processing inequality, and can be related to the optimal error exponents in composite hypothesis testing. The second measure does not satisfy the data-processing inequality, but appears naturally in the context of distributed task encoding.

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