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Copula (statistics)
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<p>In <a href="/facts/Probability_theory/mFBn51rE">probability theory</a> and <a href="/facts/Statistics/DNPsKGYU">statistics</a>, a copula is a multivariate <a href="/facts/Cumulative_distribution_function/WaKU8tp4">cumulative distribution function</a> for which the <a href="/facts/Marginal_probability/U9XBWAd1">marginal probability</a> distribution of each variable is <a href="/facts/Uniform_distribution_(continuous)/XbnlVljT">uniform</a> on the interval [0, 1]. Copulas are used to describe / model the <a href="/facts/Dependent_and_independent_variables/dINfzIF2">dependence</a> (inter-correlation) between <a href="/facts/Random_variable/TwTBXnLT">random variables</a>. Their name, introduced by applied mathematician <a href="/facts/Abe_Sklar/Q3aJ2Wxe">Abe Sklar</a> in 1959, comes from the Latin for "link" or "tie", similar but only metaphoricly related to grammatical <a href="/facts/Copula_(linguistics)/nFkAhQhL">copulas</a> in <a href="/facts/Linguistics/oQuamvf2">linguistics</a>. Copulas have been used widely in <a href="/facts/Quantitative_finance/P9otEltQ">quantitative finance</a> to model and minimize tail risk and <a href="/facts/Portfolio_optimization/H1rvkwzX">portfolio-optimization</a> applications. </p><p>Sklar's theorem states that any multivariate <a href="/facts/Cumulative_distribution_function/WaKU8tp4">joint distribution</a> can be written in terms of univariate <a href="/facts/Marginal_distribution/U9XBWAd1">marginal distribution</a> functions and a copula which describes the dependence structure between the variables. </p><p>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 copulas 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. </p><p>Two-dimensional copulas are known in some other areas of mathematics under the name <i>permutons</i> and <i>doubly-stochastic measures</i>. </p>