Copula (probability theory) (nonfiction): Difference between revisions
No edit summary |
|||
| (One intermediate revision by the same user not shown) | |||
| Line 1: | Line 1: | ||
In [[Probability theory (nonfiction)|probability theory]] and [[Statistics (nonfiction)|statistics]], a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. Copulas are used to describe the dependence between random variables. Their name comes from the Latin for "link" or "tie", similar but unrelated to grammatical copulas in linguistics[citation needed]. Copulas have been used widely in quantitative finance to model and minimize tail risk and portfolio-optimization applications. | In [[Probability theory (nonfiction)|probability theory]] and [[Statistics (nonfiction)|statistics]], a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. | ||
== Description == | |||
Copulas are used to describe the dependence between random variables. Their name comes from the Latin for "link" or "tie", similar but unrelated to grammatical copulas in linguistics[citation needed]. Copulas have been used widely in quantitative finance to model and minimize tail risk and portfolio-optimization applications. | |||
Sklar's theorem states that any multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula which describes the dependence structure between the variables. | Sklar's theorem states that any multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula which describes the dependence structure between the variables. | ||
| Line 24: | Line 28: | ||
* [[Coupling (probability) (nonfiction)]] - a proof technique that allows one to compare two unrelated random variables (distributions) in a particularly desirable way. See [[Multivariate random variable (nonfiction)|Multivariate random variable]] | * [[Coupling (probability) (nonfiction)]] - a proof technique that allows one to compare two unrelated random variables (distributions) in a particularly desirable way. See [[Multivariate random variable (nonfiction)|Multivariate random variable]] | ||
* [[Mathematics (nonfiction)]] | * [[Mathematics (nonfiction)]] | ||
* [[Sklar's theorem (nonfiction)]] - provides the theoretical foundation for the application of copulas; named after [[Abe Sklar (nonfiction)|Abe Sklar]], | |||
== External links == | == External links == | ||
Latest revision as of 06:18, 10 May 2020
In probability theory and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1].
Description
Copulas are used to describe the dependence between random variables. Their name comes from the Latin for "link" or "tie", similar but unrelated to grammatical copulas in linguistics[citation needed]. Copulas have been used widely in quantitative finance to model and minimize tail risk and portfolio-optimization applications.
Sklar's theorem states that any multivariate joint distribution can be written in terms of univariate marginal distribution functions and a copula which describes the dependence structure between the variables.
Copulas are popular in high-dimensional statistical applications as they allow one to easily model and estimate the distribution of random vectors by estimating marginals and copulae separately. There are many parametric copula families available, which usually have parameters that control the strength of dependence. Some popular parametric copula models are outlined below.
Two-dimensional copulas are known in some other areas of mathematics under the name permutons and doubly-stochastic measures.
In the News
Fiction cross-reference
- Count Copula - fictional mathematics tutor satirizing Count Chocula.
- "One variable of uniform marginal probability distribution on the interval [0, 1] ... *two* variables of uniform marginal probability distribution on the interval [0, 1] ...."
- Crimes against mathematical constants
- Gnomon algorithm
- Gnomon Chronicles
- Mathematics
Nonfiction cross-reference
- Coupling (probability) (nonfiction) - a proof technique that allows one to compare two unrelated random variables (distributions) in a particularly desirable way. See Multivariate random variable
- Mathematics (nonfiction)
- Sklar's theorem (nonfiction) - provides the theoretical foundation for the application of copulas; named after Abe Sklar,
External links
- Copula (probability theory) @ Wikipedia