Quadratic mixture identities and strict convexity of Chatterjee's xi #
Mixture weights stay constant under conditioning because every first marginal is uniform. Xi is a quadratic, strictly convex functional of the copula, and mixing with independence scales xi by the square of the retained weight.
theorem
ProbabilityTheory.Copula.chatterjeeXi_mix
(C D : Copula 2)
(a : ↑unitInterval)
:
(C.mix D a).chatterjeeXi = ↑a ^ 2 * C.chatterjeeXi + (1 - ↑a) ^ 2 * D.chatterjeeXi + 2 * ↑a * (1 - ↑a) * C.chatterjeeCross D
theorem
ProbabilityTheory.Copula.chatterjeeXi_mix_eq_sub_distance
(C D : Copula 2)
(a : ↑unitInterval)
:
(C.mix D a).chatterjeeXi = ↑a * C.chatterjeeXi + (1 - ↑a) * D.chatterjeeXi - 6 * ↑a * (1 - ↑a) * C.conditionalCDFDistanceSq D
The exact nonnegative defect in the convexity inequality.
theorem
ProbabilityTheory.Copula.chatterjeeXi_mix_lt
{C D : Copula 2}
(hne : C ≠ D)
(a : ↑unitInterval)
(ha0 : 0 < a)
(ha1 : a < 1)
:
Every nontrivial mixture of distinct copulas gives strict convexity.
theorem
ProbabilityTheory.Copula.chatterjeeXi_mix_eq_iff
(C D : Copula 2)
(a : ↑unitInterval)
(ha0 : 0 < a)
(ha1 : a < 1)
:
Mixing with independence attenuates xi quadratically in the retained copula weight.
theorem
ProbabilityTheory.Copula.chatterjeeXi_mix_comonotonic
(C : Copula 2)
(a : ↑unitInterval)
:
(C.mix (comonotonic 2) a).chatterjeeXi = ↑a ^ 2 * C.chatterjeeXi + (1 - ↑a) ^ 2 + 2 * ↑a * (1 - ↑a) * C.spearmanFootrule
Mixing with the upper Fréchet bound links xi to Spearman's footrule.