The Lévy-Prokhorov distance on spaces of finite measures and probability measures #
Main definitions #
MeasureTheory.levyProkhorovEDist
: The Lévy-Prokhorov edistance between two measures.MeasureTheory.levyProkhorovDist
: The Lévy-Prokhorov distance between two finite measures.
Main results #
levyProkhorovDist_pseudoMetricSpace_finiteMeasure
: The Lévy-Prokhorov distance is a pseudoemetric on the space of finite measures.levyProkhorovDist_pseudoMetricSpace_probabilityMeasure
: The Lévy-Prokhorov distance is a pseudoemetric on the space of probability measures.levyProkhorov_le_convergenceInDistribution
: The topology of the Lévy-Prokhorov metric on probability measures is always at least as fine as the topology of convergence in distribution.levyProkhorov_eq_convergenceInDistribution
: The topology of the Lévy-Prokhorov metric on probability measures on a separable space coincides with the topology of convergence in distribution, and in particular convergence in distribution is then pseudometrizable.
Tags #
finite measure, probability measure, weak convergence, convergence in distribution, metrizability
Lévy-Prokhorov metric #
The Lévy-Prokhorov edistance between measures:
d(μ,ν) = inf {r ≥ 0 | ∀ B, μ B ≤ ν Bᵣ + r ∧ ν B ≤ μ Bᵣ + r}
.
Equations
- One or more equations did not get rendered due to their size.
Instances For
A general sufficient condition for bounding levyProkhorovEDist
from above.
A simple general sufficient condition for bounding levyProkhorovEDist
from above.
The Lévy-Prokhorov distance between finite measures:
d(μ,ν) = inf {r ≥ 0 | ∀ B, μ B ≤ ν Bᵣ + r ∧ ν B ≤ μ Bᵣ + r}
.
Equations
- MeasureTheory.levyProkhorovDist μ ν = (MeasureTheory.levyProkhorovEDist μ ν).toReal
Instances For
A type synonym, to be used for Measure α
, FiniteMeasure α
, or ProbabilityMeasure α
,
when they are to be equipped with the Lévy-Prokhorov distance.
Equations
Instances For
The Lévy-Prokhorov distance levyProkhorovEDist
makes Measure Ω
a pseudoemetric
space. The instance is recorded on the type synonym LevyProkhorov (Measure Ω) := Measure Ω
.
Equations
- MeasureTheory.instPseudoEMetricSpaceLevyProkhorovMeasure = PseudoEMetricSpace.mk ⋯ ⋯ ⋯ (uniformSpaceOfEDist MeasureTheory.levyProkhorovEDist ⋯ ⋯ ⋯) ⋯
The Lévy-Prokhorov distance levyProkhorovDist
makes FiniteMeasure Ω
a pseudometric
space. The instance is recorded on the type synonym
LevyProkhorov (FiniteMeasure Ω) := FiniteMeasure Ω
.
Equations
- One or more equations did not get rendered due to their size.
Two measures at vanishing Lévy-Prokhorov distance from each other assign the same values to all closed sets.
The Lévy-Prokhorov distance levyProkhorovDist
makes ProbabilityMeasure Ω
a pseudometric
space. The instance is recorded on the type synonym
LevyProkhorov (ProbabilityMeasure Ω) := ProbabilityMeasure Ω
.
Note: For this pseudometric to give the topology of convergence in distribution, one must
furthermore assume that Ω
is separable.
Equations
- One or more equations did not get rendered due to their size.
If Ω
is a Borel space, then the Lévy-Prokhorov distance levyProkhorovDist
makes
ProbabilityMeasure Ω
a metric space. The instance is recorded on the type synonym
LevyProkhorov (ProbabilityMeasure Ω) := ProbabilityMeasure Ω
.
Note: For this metric to give the topology of convergence in distribution, one must
furthermore assume that Ω
is separable.
Equations
- MeasureTheory.levyProkhorovDist_metricSpace_probabilityMeasure = MetricSpace.mk ⋯
A simple sufficient condition for bounding levyProkhorovEDist
between probability measures
from above. The condition involves only one of two natural bounds, the other bound is for free.
A simple sufficient condition for bounding levyProkhorovDist
between probability measures
from above. The condition involves only one of two natural bounds, the other bound is for free.
The Lévy-Prokhorov topology is at least as fine as convergence in distribution #
A version of the layer cake formula for bounded continuous functions which have finite integral: ∫ f dμ = ∫ t in (0, ‖f‖], μ {x | f(x) ≥ t} dt.
A version of the layer cake formula for bounded continuous functions and finite measures: ∫ f dμ = ∫ t in (0, ‖f‖], μ {x | f(x) ≥ t} dt.
Assuming levyProkhorovEDist μ ν < ε
, we can bound ∫ f ∂μ
in terms of
∫ t in (0, ‖f‖], ν (thickening ε {x | f(x) ≥ t}) dt
and ‖f‖
.
A monotone decreasing convergence lemma for integrals of measures of thickenings:
∫ t in (0, ‖f‖], μ (thickening ε {x | f(x) ≥ t}) dt
tends to
∫ t in (0, ‖f‖], μ {x | f(x) ≥ t} dt
as ε → 0
.
The identity map LevyProkhorov (ProbabilityMeasure Ω) → ProbabilityMeasure Ω
is continuous.
The topology of the Lévy-Prokhorov metric is at least as fine as the topology of convergence in distribution.
On separable spaces the Lévy-Prokhorov distance metrizes convergence in distribution #
In a separable pseudometric space, for any ε > 0 there exists a countable collection of disjoint Borel measurable subsets of diameter at most ε that cover the whole space.
The topology of the Lévy-Prokhorov metric on probability measures on a separable space coincides with the topology of convergence in distribution.
The identity map is a homeomorphism from ProbabilityMeasure Ω
with the topology of
convergence in distribution to ProbabilityMeasure Ω
with the Lévy-Prokhorov (pseudo)metric.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The topology of convergence in distribution on a separable space is pseudo-metrizable.
The topology of convergence in distribution on a separable Borel space is metrizable.