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On the Hausdorff measure of noncompactness for the parameterized Prokhorov metric
Journal of Inequalities and Applications volume 2016, Article number: 215 (2016)
Abstract
We quantify the Prokhorov theorem by establishing an explicit formula for the Hausdorff measure of noncompactness (HMNC) for the parameterized Prokhorov metric on the set of Borel probability measures on a Polish space. Furthermore, we quantify the Arzelà-Ascoli theorem by obtaining upper and lower estimates for the HMNC for the uniform norm on the space of continuous maps of a compact interval into Euclidean N-space, using Jung’s theorem on the Chebyshev radius. Finally, we combine the obtained results to quantify the stochastic Arzelà-Ascoli theorem by providing upper and lower estimates for the HMNC for the parameterized Prokhorov metric on the set of multivariate continuous stochastic processes.
1 Introduction and statement of the main results
For the basic probabilistic concepts and results, we refer the reader to any standard work on probability theory such as [1].
Let S be a Polish space, that is, a separable completely metrizable topological space, and \(\mathcal{P}(S)\) the collection of Borel probability measures on S, equipped with the weak topology \(\tau_{w}\), that is, the weakest topology for which each map
with bounded and continuous \(f : S \rightarrow\mathbb{R}\) is continuous. The space \(\mathcal{P}(S)\) is known to be Polish.
We call a collection \(\Gamma\subset\mathcal{P}(S)\) uniformly tight iff for each \(\epsilon> 0\), there exists a compact set \(K \subset S\) such that \(P(S \setminus K) < \epsilon\) for all \(P \in\Gamma\).
The following celebrated result interrelates the \(\tau_{w}\)-relative compactness with uniform tightness.
Theorem 1.1
(Prokhorov)
A collection \(\Gamma\subset\mathcal{P}(S)\) is \(\tau_{w}\)-relatively compact if and only if it is uniformly tight.
Fix \(N \in\mathbb{N}_{0}\) and let \(\mathcal{C}\) be the space of continuous maps x of the compact interval \([0,1]\) into Euclidean N-space \(\mathbb{R}^{N}\) equipped with the uniform topology \(\tau _{\infty}\), that is, the topology derived from the uniform norm
where \(\vert \cdot \vert \) stands for the Euclidean norm. The space \(\mathcal{C}\) is also known to be Polish.
Recall that a set \(\mathcal{X} \subset\mathcal{C}\) is said to be uniformly bounded iff there exists a constant \(M > 0\) such that \(\vert x(t)\vert \leq M\) for all \(x \in\mathcal{X}\) and \(t \in [0,1 ]\), and uniformly equicontinuous iff for each \(\epsilon> 0\), there exists \(\delta> 0\) such that \(\vert x(s) - x(t)\vert < \epsilon\) for all \(x \in\mathcal{X}\) and \(s,t \in [0,1 ]\) with \(\vert s - t\vert < \delta\).
In this setting, the following theorem is a classical result [2].
Theorem 1.2
(Arzelà-Ascoli)
A collection \(\mathcal{X} \subset\mathcal{C}\) is \(\tau_{\infty}\)-relatively compact if and only if it is uniformly bounded and uniformly equicontinuous.
Let \(\Omega= (\Omega,\mathcal{F},\mathbb{P})\) be a fixed probability space. Throughout, a continuous stochastic process (c.s.p.) is a Borel-measurable map of Ω into \(\mathcal{C}\), and we consider on the set of c.s.p.s the weak topology \(\tau_{w}\), that is, the topology with open sets \(\{\xi\text{ c.s.p.} \mid\mathbb{P}_{\xi}\in\mathcal{G}\}\), where \(\mathbb{P}_{\xi}\) is the probability distribution of ξ, and \(\mathcal{G}\) is a \(\tau_{w}\)-open set in \(\mathcal{P} (\mathcal{C} )\).
A collection Ξ of c.s.p.s is said to be stochastically uniformly bounded iff for each \(\epsilon> 0\), there exists \(M > 0\) such that \(\mathbb{P} (\|\xi\|_{\infty}> M ) < \epsilon\) for all \(\xi\in\Xi\), and stochastically uniformly equicontinuous iff for all \(\epsilon, \epsilon^{\prime}> 0\), there exists \(\delta> 0\) such that \(\mathbb{P} (\sup_{\vert s - t\vert < \delta} \vert \xi (s) - \xi(t)\vert \geq\epsilon ) < \epsilon^{\prime}\) for all \(\xi\in\Xi\), the supremum taken over all \(s,t \in[0,1]\) for which \(\vert s - t\vert < \delta\).
It is not hard to see that combining Theorem 1.1 and Theorem 1.2 yields the following stochastic version of Theorem 1.2, which plays a crucial role in the development of functional central limit theory.
Theorem 1.3
(Stochastic Arzelà-Ascoli)
A collection Ξ of c.s.p.s is \(\tau_{w}\)-relatively compact if and only if it is stochastically uniformly bounded and stochastically uniformly equicontinuous.
In a complete metric space \((X,d)\), the Hausdorff measure of noncompactness of a set \(A \subset X\) [3, 4] is given by
the first infimum running through all finite sets \(F \subset X\). It is well known that A is d-bounded if and only if \(\mu_{ \mathrm{H},d}(A) < \infty\), and d-relatively compact if and only if \(\mu_{{\mathrm{H}},d}(A) = 0\).
Fix a complete metric d metrizing the topology of the Polish space S. The Prokhorov distance with parameter \(\lambda\in\mathbb {R}^{+}_{0}\) between probability measures \(P,Q \in\mathcal{P}(S)\) [5] is defined as the infimum of all numbers \(\alpha\in\mathbb {R}^{+}_{0}\) such that
for all Borel sets \(A \subset S\), where
This distance is denoted by \(\rho_{\lambda}(P,Q)\). It defines a complete metric on \(\mathcal{P}(S)\) and induces the weak topology \(\tau_{w}\). It is also known that \(\rho_{\lambda_{1}} \leq\rho _{\lambda_{2}}\) if \(\lambda_{1} \geq\lambda_{2}\) and that
the supremum being taken over all Borel sets \(A \subset S\).
For a collection \(\Gamma\subset\mathcal{P}(S)\), we define the measure of nonuniform tightness as
where the infimum runs through all finite sets \(Y \subset S\), and
It is clear that \(\mu_{\mathrm{ut}}(\Gamma) = 0\) if Γ is uniformly tight. The converse holds as well. Indeed, suppose that \(\mu_{\mathrm{ut}}(\Gamma) = 0\) and fix \(\epsilon> 0\). Then, for each \(n \in\mathbb{N}_{0}\), choose a finite set \(Y_{n} \subset S\) such that
for all \(P \in\Gamma\). Put
with \(B^{\star}(y,1/n)\) the closure of \(B(y,1/n)\). Then K is a compact set such that \(P(S \setminus K) < \epsilon\) for all \(P \in\Gamma\). We conclude that Γ is uniformly tight. The measure \(\mu_{\mathrm {ut}}\) is slightly weaker than the weak measure of tightness studied in [6].
By the previous considerations we know that a set \(\Gamma\subset \mathcal{P}(S)\) is \(\tau_{w}\)-relatively compact if and only if \(\mu _{\mathrm{H},\rho_{\lambda}}(\Gamma) = 0\) for each \(\lambda \in\mathbb{R}^{+}_{0}\), and uniformly tight if and only if \(\mu_{\mathrm {ut}}(\Gamma) = 0\). Therefore, Theorem 1.4, our first main result, which provides a quantitative relation between the numbers \(\mu_{\mathrm{H},\rho_{\lambda}}(\Gamma)\) and \(\mu _{\mathrm{ut}}(\Gamma)\), is a strict generalization of Theorem 1.1. The proof is given in Section 2.
Theorem 1.4
(Quantitative Prokhorov)
For a collection \(\Gamma\subset\mathcal{P}(S)\),
From now on, we consider on the space \(\mathcal{C}\) the uniform metric derived from the uniform norm, and for a set \(\mathcal{X} \subset \mathcal{C}\), we let \(\mu_{\mathrm{H},\infty}(\mathcal{X})\) stand for the Hausdorff measure of noncompactness; more precisely,
the infimum taken over all finite sets \(\mathcal{F} \subset\mathcal {C}\). Clearly, \(\mathcal{X}\) is \(\tau_{\infty}\)-relatively compact if and only if \(\mu_{\mathrm{H},\infty}(\mathcal{X}) = 0\).
The measure of nonuniform equicontinuity of \(\mathcal{X} \subset \mathcal{C}\) is defined by
the second supremum running through all \(s,t \in[0,1]\) with \(\vert s - t\vert < \delta\). We readily see that \(\mathcal{X}\) is uniformly equicontinuous if and only if \(\mu_{\mathrm{uec}}(\mathcal {X}) = 0\). In [3], it was shown that \(\mu_{\mathrm{ {uec}}}\) is a measure of noncompactness on the space \(\mathcal{C}\) (Theorem 11.2).
Theorem 1.5, our second main result, entails that the measures \(\mu_{\mathrm{H},\infty}\) and \(\mu_{\mathrm {uec}}\) are Lipschitz equivalent on the collection of uniformly bounded subsets of \(\mathcal{C}\), and thus it strictly generalizes Theorem 1.2. The proof, which hinges upon a classical result of Jung on the Chebyshev radius, is given in Section 3.
Theorem 1.5
(Quantitative Arzelà-Ascoli)
For \(\mathcal{X} \subset\mathcal{C}\),
Suppose, in addition, that \(\mathcal{X}\) is uniformly bounded. Then
In particular, if \(N = 1\), then
and, regardless of N,
We transport the parameterized Prokhorov metric from \(\mathcal {P}(\mathcal{C})\) to the collection of c.s.p.s via their probability distributions. Thus, for c.s.p.s ξ and η,
Note that a set of c.s.p.s Ξ is \(\tau_{\omega}\)-relatively compact if and only if \(\mu_{\mathrm{H},\rho_{\lambda}}(\Xi) = 0\) for all \(\lambda\in\mathbb{R}^{+}_{0}\).
For a set of c.s.p.s Ξ, the measure of nonstochastic uniform boundedness is given by
and the measure of nonstochastic uniform equicontinuity by
where the third supremum is taken over all \(s,t \in[0,1]\) with \(\vert s - t\vert < \delta\). It is easily seen that Ξ is stochastically uniformly bounded if and only if \(\mu_{\mathrm{sub}}(\Xi) = 0\), and stochastically uniformly equicontinuous if and only if \(\mu_{\mathrm{suec}}(\Xi) = 0\). The measure \(\mu _{\mathrm{suec}}\) was studied in [6].
In Section 4, we prove that combining Theorem 1.4 and Theorem 1.5 leads to Theorem 1.6, our third main result, which gives upper and lower bounds for \(\sup_{\lambda\in\mathbb{R}^{+}_{0}} \mu_{\mathrm{H},\rho _{\lambda}}\) in terms of \(\mu_{\mathrm{sub}}\) and \(\mu_{\mathrm{suec}}\). Theorem 1.6 strictly generalizes Theorem 1.3.
Theorem 1.6
(Quantitative stochastic Arzelà-Ascoli)
Let Ξ be a collection of c.s.p.s. Then
In particular, if Ξ is stochastically uniformly bounded, then
and, if Ξ is stochastically uniformly equicontinuous, then
2 Proof of Theorem 1.4
For a collection \(\Gamma\subset\mathcal{P}(S)\), put
and
We first show that
with an argument that essentially refines the first part of the proof of Theorem 4.9 in [6].
Fix \(\lambda\in\mathbb{R}^{+}_{0}\), \(\epsilon> 0\), and choose pairwise disjoint Borel sets
with diameters less than λϵ such that
Then, for each \(i \in\{1,\ldots,n\}\), pick \(x_{i} \in A_{i}\), and, assuming without loss of generality that \(S \setminus\bigcup_{i=1}^{n} A_{i}\) is nonempty, \(x_{n+1} \in S \setminus\bigcup_{i=1}^{n} A_{i}\). Finally, fix \(m \in\mathbb{N}_{0}\) such that
and let Φ be a finite collection of Borel probability measures on S of the form
where the \(k_{i}\) range in \(\{0,\ldots,m\}\) so that
and \(\delta_{x_{i}}\) stands for the Dirac probability measure putting all its mass on \(x_{i}\).
We now claim that
which finishes the proof of the desired inequality.
To prove the claim, take \(P \in\Gamma\) and construct
in Φ such that
for all \(i \in\{1,\ldots,n\}\). For a Borel set \(A \subset S\), let I stand for the set of those \(i \in\{1,\ldots,n\}\) for which \(A_{i} \cap A\) is nonempty. Then we derive from the calculation
that
establishing the claim.
We now show that
Fix \(\epsilon, \epsilon^{\prime}> 0\). Choose \(\lambda\in\mathbb {R}^{+}_{0}\) such that
and take a finite collection \(\Phi\subset\mathcal{P}(S)\) such that, for each \(P \in\Gamma\), there exists \(Q \in\Phi\) for which
The collection Φ being finite, we can pick a finite set \(Y \subset S\) such that
We claim that
proving the desired inequality.
To establish the claim, take \(P \in\Gamma\), and let Q be a probability measure in Φ such that
Then
which finishes the proof of the claim.
3 Proof of Theorem 1.5
Before writing down the proof of Theorem 1.5, we give the required preparation.
For a bounded set \(A \subset\mathbb{R}^{N}\), its diameter is given by
and the Chebyshev radius by
It is well known that, for each bounded set \(A \subset\mathbb{R}^{N}\), there exists a unique \(x_{A} \in\mathbb{R}^{N}\) such that
The point \(x_{A}\) is called the Chebyshev center of A. A good exposition of the previous notions in a general normed vector space can be found in [7], Section 33.
Theorem 3.1 provides a relation between the diameter and the Chebyshev radius of a bounded set in \(\mathbb{R}^{N}\). A beautiful proof can be found in [8]. For extensions of the result, we refer to [9–11], and [12].
Theorem 3.1
(Jung)
Let \(A \subset\mathbb{R}^{N}\) be a bounded set. Then
We need two additional simple lemmas on linear interpolation.
For \(c_{0} \in\mathbb{R}^{N}\) and \(r \in\mathbb{R}_{0}^{+}\), we denote by \(B^{\star}(c_{0},r)\) the closed ball with center \(c_{0}\) and radius r.
Lemma 3.2
Consider \(c_{1},c_{2} \in\mathbb{R}^{N}\) and \(r \in\mathbb{R}^{+}_{0}\), and assume that
Let L be the \(\mathbb{R}^{N}\)-valued map on the compact interval \([\alpha,\beta ]\) defined by
Then, for all \(t \in [\alpha,\beta ]\) and \(y \in B^{\star}(c_{1},r) \cap B^{\star}(c_{2},r)\),
Proof
The calculation
proves the lemma. □
Lemma 3.3
Consider \(c_{1},c_{2},y_{1},y_{2} \in\mathbb{R}^{N}\) and \(\epsilon> 0\), and suppose that
and
Let L and M be the \(\mathbb{R}^{N}\)-valued maps on the compact interval \([\alpha,\beta ]\) defined by
and
Then
Proof
It is analogous to the proof of Lemma 3.2. □
Proof of Theorem 1.5
We first prove that
Let \(\alpha> 0\) be such that \(\mu_{\mathrm{H},\infty} (\mathcal{X} ) < \alpha\). Then there exists a finite set \(\mathcal{F} \subset\mathcal{C}\) such that, for all \(x \in\mathcal {X}\), there exists \(y \in\mathcal{F}\) for which \(\|y - x\|_{\infty}\leq\alpha\). Take \(\epsilon> 0\). Since \(\mathcal{F}\) is uniformly equicontinuous, there exists \(\delta> 0\) such that
Now, for \(x \in\mathcal{X}\), choose \(y \in\mathcal{F}\) such that
Then, for \(s , t \in [0,1 ]\) with \(\vert s - t\vert < \delta\), we have, by (1) and (2),
which, by the arbitrariness of ϵ, reveals that \(\mu_{\mathrm {uec}} (\mathcal{X} ) \leq2\alpha\), and thus, by the arbitrariness of α, we have the inequality
Next, assume that \(\mathcal{X} \subset\mathcal{C}\) is uniformly bounded. We show that
Fix \(\epsilon> 0\). Then, \(\mathcal{X}\) being uniformly bounded, we can take a constant \(M> 0\) such that
Pick a finite set \(Y \subset\mathbb{R}^{N}\) for which
Now let
be such that \(\mu_{\mathrm{uec}}(\mathcal{X}) < \alpha\), that is, there exists \(\delta> 0\) such that
Then choose points
put
and assume that we have made this choice such that
Furthermore, for each \((y_{0}, \ldots, y_{2n+ 1}) \in Y^{2n+2}\), let \(L_{(y_{0}, \ldots, y_{2n+1})}\) be the \(\mathbb{R}^{N}\)-valued map on \([0,1 ]\) defined by
and put
Then \(\mathcal{F}\) is a finite subset of \(\mathcal{C}\). Now fix \(x \in\mathcal{X}\) and let \(c_{x,k}\) stand for the Chebyshev center of \(x(I_{k})\) for each \(k \in \{0,\ldots, n \}\). It follows from (6) and (7) that \(\operatorname{diam}f(I_{k}) \leq\alpha\), and thus, by Theorem 3.1,
Let x̃ be the \(\mathbb{R}^{N}\)-valued map on \([0,1 ]\) defined by
Then (8) and Lemma 3.2 yield that
Also, it easily follows from (3), (5), and (9) that \(\|\tilde{x}\|_{\infty} \leq3M\), and thus (4) allows us to choose \((y_{0}, \ldots, y_{2n + 1} ) \in Y^{2n + 2}\) such that
Combining (10) and Lemma 3.3 reveals that
Thus, we have found \(L_{(y_{0},\ldots,y_{2n + 1})}\) in \(\mathcal{F}\) for which, by (9) and (11),
which, by the arbitrariness of ϵ, entails that \(\mu_{\mathrm{H},\infty}(\mathcal{F}) \leq (\frac{N}{2N + 2} )^{1/2} \alpha\), and thus, by the arbitrariness of α, the inequality
is established. □
4 Proof of Theorem 1.6
We transport the measure of nonuniform tightness from \(\mathcal {P}(\mathcal{C})\) to the collection of c.s.p.s via their probability distributions. Thus, for a set Ξ of c.s.p.s,
where the infimum is taken over all finite sets \(\mathcal{F} \subset \mathcal{C}\), and
Before giving the proof of Theorem 1.6, we state three lemmas, which are easily seen to follow from the definitions.
Lemma 4.1
Let Ξ be a collection of c.s.p.s, and \(\alpha\in\mathbb {R}^{+}_{0}\). Then the following assertions are equivalent.
-
(1)
\(\mu_{\mathrm{ut}}(\Xi) < \alpha\).
-
(2)
For each \(\epsilon> 0\), there exists a uniformly bounded set \(\mathcal{X} \subset\mathcal{C}\) such that
-
(a)
\(\mu_{\mathrm{H},\infty}(\mathcal{X}) < \epsilon\),
-
(b)
\(\forall\xi\in\Xi\): \(\mathbb{P}(\xi\notin\mathcal{X}) < \alpha\).
-
(a)
Lemma 4.2
Let Ξ be a collection of c.s.p.s, and \(\alpha\in\mathbb {R}^{+}_{0}\). Then the following assertions are equivalent.
-
(1)
\(\mu_{\mathrm{sub}}(\Xi) < \alpha\).
-
(2)
There exists a uniformly bounded set \(\mathcal{X} \subset \mathcal{C}\) such that
$$\forall\xi\in\Xi\mbox{:}\quad \mathbb{P}(\xi\notin\mathcal{X}) < \alpha. $$
Lemma 4.3
Let Ξ be a collection of c.s.p.s, and \(\alpha\in\mathbb {R}^{+}_{0}\). Then the following assertions are equivalent.
-
(1)
\(\mu_{\mathrm{suec}}(\Xi) < \alpha\).
-
(2)
For each \(\epsilon> 0\), there exists a set \(\mathcal{X} \subset \mathcal{C}\) such that
-
(a)
\(\mu_{\mathrm{uec}}(\mathcal{X}) < \epsilon\),
-
(b)
\(\forall\xi\in\Xi\): \(\mathbb{P}(\xi\notin\mathcal{X}) < \alpha\).
-
(a)
Proof of Theorem 1.6
Let Ξ be a collection of c.s.p.s. By Theorem 1.4,
whence it suffices to show that
We first establish that
Fix \(\epsilon> 0\) and \(\alpha,\beta\in\mathbb{R}^{+}_{0}\) such that
and
By Lemma 4.2 there exists a uniformly bounded set \(\mathcal{Y} \subset\mathcal{C}\) such that
and by Lemma 4.3 there exists a set \(\mathcal{Z} \subset\mathcal{C}\) such that
and
Put
Then \(\mathcal{X}\) is uniformly bounded. Also, by Theorem 1.5 and (12),
and, for \(\xi\in\Xi\),
We conclude from Lemma 4.1 that
from which the desired inequality follows.
Next, we prove that
Fix \(\epsilon> 0\) and \(\alpha\in\mathbb{R}^{+}_{0}\) such that
By Lemma 4.1 there exists a uniformly bounded set \(\mathcal{X} \subset\mathcal{C}\) such that
and
We conclude from Lemma 4.2 that
Moreover, by Theorem 1.5 and (13),
and Lemma 4.3 allows us to infer that
which finishes the proof of the desired inequality. □
5 Conclusions
In this work, we have quantified the Prokhorov theorem by establishing an explicit formula for the Hausdorff measure of noncompactness (HMNC) for the parameterized Prokhorov metric on the set of Borel probability measures on a Polish space (Theorem 1.4). Furthermore, we have quantified the Arzelà-Ascoli theorem by obtaining upper and lower estimates for the HMNC for the uniform norm on the space of continuous maps of a compact interval into Euclidean N-space, using the Jung theorem on the Chebyshev radius (Theorem 1.5). Finally, we have combined the obtained results to quantify the stochastic Arzelà-Ascoli theorem by providing upper and lower estimates for the HMNC for the parameterized Prokhorov metric on the set of multivariate continuous stochastic processes (Theorem 1.6). This work fits nicely in the research initiated in [6], the aim of which is to systematically study quantitative measures, such as the HMNC, in the realm of probability theory.
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Acknowledgements
The author thanks Mark Sioen for interesting discussions concerning the topics in this work and the Fund for Scientific Research Flanders (FWO) for its financial support.
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Ben Berckmoes is post doctoral fellow at the Fund for Scientific Research of Flanders (FWO).
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Berckmoes, B. On the Hausdorff measure of noncompactness for the parameterized Prokhorov metric. J Inequal Appl 2016, 215 (2016). https://doi.org/10.1186/s13660-016-1151-8
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DOI: https://doi.org/10.1186/s13660-016-1151-8