distance covariance
E472795
measure of dependence
multivariate dependence measure
nonparametric dependence measure
statistical measure
Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
All labels observed (1)
| Label | Occurrences |
|---|---|
| distance covariance canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4829112 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: distance covariance Context triple: [Gábor J. Székely, notableConcept, distance covariance]
-
A.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
B.
Kolmogorov distance
Kolmogorov distance is a statistical metric that measures the maximum difference between two cumulative distribution functions, commonly used to quantify convergence in distribution and in goodness-of-fit tests.
-
C.
Hellinger distance
Hellinger distance is a statistical measure of dissimilarity between probability distributions, derived from the Euclidean distance between their square-root densities and widely used in probability theory and information geometry.
-
D.
Kullback–Leibler divergence
Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
-
E.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: distance covariance Target entity description: Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
-
A.
Bhattacharyya distance
Bhattacharyya distance is a statistical measure of similarity between two probability distributions, often used in pattern recognition and classification to quantify their overlap.
-
B.
Kolmogorov distance
Kolmogorov distance is a statistical metric that measures the maximum difference between two cumulative distribution functions, commonly used to quantify convergence in distribution and in goodness-of-fit tests.
-
C.
Hellinger distance
Hellinger distance is a statistical measure of dissimilarity between probability distributions, derived from the Euclidean distance between their square-root densities and widely used in probability theory and information geometry.
-
D.
Kullback–Leibler divergence
Kullback–Leibler divergence is a fundamental information-theoretic measure that quantifies how one probability distribution differs from a reference distribution.
-
E.
Hotelling’s T-squared distribution
Hotelling’s T-squared distribution is a multivariate generalization of Student’s t-distribution used primarily for hypothesis testing and constructing confidence regions for mean vectors in multivariate statistics.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
measure of dependence
ⓘ
multivariate dependence measure ⓘ nonparametric dependence measure ⓘ statistical measure ⓘ |
| advantageOver | Pearson correlation in detecting nonlinear dependence ⓘ |
| belongsTo |
dependence modeling
ⓘ
multivariate statistics ⓘ nonparametric statistics ⓘ |
| canDetect |
linear dependence
ⓘ
nonlinear dependence ⓘ |
| comparedWith |
Pearson correlation
ⓘ
mutual information ⓘ |
| dependsOn | Euclidean distances between sample points ⓘ |
| equalsZeroFor | independent random variables ⓘ |
| generalizes | classical covariance in detecting dependence ⓘ |
| greaterThanZeroFor | dependent random variables ⓘ |
| hasExtension |
conditional distance covariance
ⓘ
distance covariance for time series ⓘ partial distance covariance ⓘ |
| hasNormalizedForm | distance correlation ⓘ |
| hasProperty | characterizes independence in Euclidean spaces with finite first moments ⓘ |
| implementedIn |
Python libraries
ⓘ
R packages ⓘ |
| introducedBy |
Gábor J. Székely
ⓘ
Maria L. Rizzo ⓘ |
| introducedIn | 2007 ⓘ |
| invariantUnder |
orthogonal transformations of the data
ⓘ
translations of the data ⓘ |
| isBasedOn | pairwise distances between observations ⓘ |
| isDefinedFor |
multivariate random variables
ⓘ
random vectors in arbitrary dimensions ⓘ univariate random variables ⓘ |
| isEstimatedBy | sample distance covariance ⓘ |
| isNonNegative | true ⓘ |
| isZeroIfAndOnlyIf | random variables are independent ⓘ |
| publishedIn | Annals of Statistics ⓘ |
| quantifies | dependence between random variables ⓘ |
| relatedTo |
Brownian covariance
ⓘ
distance correlation ⓘ |
| requires |
choice of metric space for the variables
ⓘ
finite first moments of the random variables ⓘ |
| symmetricIn | its two arguments ⓘ |
| usedFor |
feature screening
ⓘ
measuring association in high dimensions ⓘ testing independence ⓘ variable selection ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: distance covariance Description of subject: Distance covariance is a statistical measure that quantifies dependence between random variables, capable of detecting both linear and nonlinear associations.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.