Conditional quantiles for pair-copula constructions #
The generalized inverse of a copula's conditional distribution is jointly measurable and samples its second coordinate given its first. No density or strict monotonicity assumption is needed.
noncomputable def
ProbabilityTheory.Copula.conditionalCDFUnit
(C : Copula 2)
(u v : ↑unitInterval)
:
The conditional CDF, with its value bundled in the unit interval.
Equations
- C.conditionalCDFUnit u v = ⟨C.conditionalCDF u v, ⋯⟩
Instances For
noncomputable def
ProbabilityTheory.Copula.conditionalQuantile
(C : Copula 2)
(u t : ↑unitInterval)
:
The generalized conditional quantile of coordinate 1 given coordinate 0.
Equations
- C.conditionalQuantile u t = ProbabilityTheory.unitQuantile (C.conditionalKernel u) t
Instances For
theorem
ProbabilityTheory.Copula.conditionalQuantile_le_iff
(C : Copula 2)
(u t v : ↑unitInterval)
:
theorem
ProbabilityTheory.Copula.monotone_conditionalQuantile
(C : Copula 2)
(u : ↑unitInterval)
:
Monotone (C.conditionalQuantile u)
@[simp]
theorem
ProbabilityTheory.Copula.map_pair_conditionalQuantile
(C : Copula 2)
:
MeasureTheory.Measure.map (fun (p : ↑unitInterval × ↑unitInterval) => ![p.1, C.conditionalQuantile p.1 p.2])
(MeasureTheory.volume.prod MeasureTheory.volume) = C.toMeasure
Conditional inverse-transform sampling recovers the entire bivariate law.
Averaging the conditional quantile over an independent uniform root is uniform.