Research Article | Open Access

# Strong Laws of Large Numbers for 𝔹 -Valued Random Fields

**Academic Editor:**Stevo Stevic

#### Abstract

We extend to random fields case, the results of Woyczynski, who proved Brunk's type strong law of large numbers (SLLNs) for -valued random vectors under geometric assumptions. Also, we give probabilistic requirements for above-mentioned SLLN, related to results obtained by Acosta as well as necessary and sufficient probabilistic conditions for the geometry of Banach space associated to the strong and weak law of large numbers for multidimensionally indexed random vectors.

#### 1. Introduction

We will study the limiting behavior of multiple sums of random vectors indexed by lattice points, so called random fields. Such research has roots in statistical mechanics and arised in the context of ergodic theory. Almost 70 years ago, Wiener considered double sums over lattice points with applications to homogenous chaos. Many aspects of present investigations of models of critical phenomena in statistical physics, crystal physics or Euclidean quantum field theories involve multiple sums of random variables with multidimensional indices. Multiparameter processes arise in applied context such as brain data imaging, and so forth.

Let , be the positive integer -dimensional lattice points with coordinate wise partial ordering, . Points in are denoted by , or more explicitly and stands for the -tuple . Also for , we define and . The notation means that or equivalently .

Let —be a probability space, —a real separable Banach space, —a family of -valued random vectors and set If then stands for the Bochner integral. Let be a -valued net and . We say that strongly as if for any , there exists such that implies or shortly for any , “occurs finitely often” (see Smythe [1], Fazekas [2]). Furthermore, let be an increasing family of sub -algebras of , that is,Now, we will introduce definition of martingale (submartingale) for real-valued random fields, Smythe [3] (for more information see Merzbach [4]). Through this paper satisfies condition CI (conditional independence)where denotes the componentwise minimum of and A-adapted, integrable process is called martingale (submartingale) ifThe main aim of this paper is to prove a couple Brunk type strong laws of large numbers for independent -valued random fields. To prove this we would like to apply, among others, maximal inequalities for real-valued submartingale fields. Main results concerning maximal inequalities for random variables indexed by multidimensional indices are due to Cairoli [5], Gabriel [6], Klesov [7], Smythe [3], Shorack and Smythe [8], as well as Wichura [9]. In [5], Cairoli gave counterexample that the well-known following Doob maximal inequality for submartingalescannot be proved for a discrete-time random fields, utilizing “one dimensional” idea, as well as Hajek-Renyi-Chow inequality [10] and in consequence Chow or Brunk type strong law of large numbers. This problem motivated us to make an effort to give some new results for strong law of large numbers for random fields.

To get the above-mentioned result we will exploit the idea of maximal inequality introduced by Christofides and Serfling [11].

Theorem 1.1. *
Let be a submartingale, satisfies (1.3), and let be a nonincreasing array of nonnegative
numbers. Then for ** where and if for some *

*Proof. *In the multidimensional martingale case, Theorem 1.1 was proved by Christofides
and Serfling [11, Theorem 2.2] using properties of submartingale fields, thus
assertion of Theorem 1.1 is true.

The following remark concerns the technique of the proof of Theorem 1.1 in martingale case.

*Remark 1.2. *
In the proof of Theorem 2.2 of [11], the authors construct the sets (see the algorithm in [11]) and say “An
explicit expression of the sets in terms of the sets is possible to derive, but such formula is
notationally messy and complicated.” It seems that in the proof, we can
use the sets constructed as follows (in the case ,
for simplicity).

Let
set and set We obtain the sets by changing the order of taking
maximum. In this construction we used idea introduced by Zimmerman [12].
Similarly to the sets constructed by Christofides and Serfling, , are disjoint, and , respectively, measurable, andSuch construction gives a simple
formula and is very intuitive.

#### 2. The Main Results

We start from the following generalization of Theorem 1.1.

Theorem 2.1. *
Let be a submartingale, satisfies (1.3), and let be a nonincreasing array of nonnegative
numbers. Then for and , ** where , and if for some *

*Proof. *
Assume
without loss of generality that the sum on right-hand side of (2.1) has minimum
for .
Let us put and define disjoint partition of as
follow:for .

It is easy to
see that and for .
Now, let us observe that we can apply Theorem 1.1 to the “cubes” ,
where . Thus we have

The next lemma is an equivalent version of the result obtained by Martikainen [13, page 435] of Kronecker lemma in multidimensional case. Let , then

Lemma 2.2. *
Let be natural numbers, with and families of increasing, positive numbers such
that strongly as and
Furthermore be an array of positive numbers such that**Then**for every *

*Proof. *
By applying the Martikainen lemma to

Lemma 2.3. *
Let be a real separable Banach space and and ,
then the following properties are equivalent.*

(i)* is R-type p.*(ii)* There exists positive constant C such that for
every and for any family of independent random vectors in with , *

*Proof. *
For (Woyczyński [14]) and
since are independent, the lemma is also valid for

Theorem 2.4. *
Let , and be field of independent, zero-mean, -valued random vectors such
that
**If is R-type p, then*

*Proof. *
Let ,
since are independent, satisfies (1.3) and is real, nonnegative submartingale. By
the definition of strong convergence of elements of and “event occurs finitely (infinitely)
often,” it is enough to prove for any . Let us observe, that by Theorem 2.1, Lemma 2.3
and Hölder's inequality for some constants , we getwhere

Now, it is enough to prove that appropriate multiple
series is finite. Changing the order of summation and comparing to integrals, for
some constant and for every , we haveThe above expression contains
the following types of sums:where , and is any subset of

Now, by Lemma 2.2, (2.13) tends to as as well as (2.14) for . Hence, we haveWe complete the proof by taking
the minimum over of both sides of (2.15), combined with (2.8) and (2.11).

For , we let obtain the following result.

Corollary 2.5. *
Let , and let be sequence of independent, zero-mean, -valued random vectors such
that **If is R-type p, then*

Corollary 2.5 for is due to Woyczyński [14], which generalized results of Hoffman-Jørgensen, Pisier and Woyczyński [15] (), and results due to Brunk [16], Prohorov [17] ().

*Example 2.6. *
Let and let be a field of random vectors fulfilling the
assumptions of Theorem 2.4. For ,
we define 2-dimensional sector as follow:Assume that are uniformly bounded by constant and Hence by comparison to integrals, we havefor

Thus, the condition (2.8) of Theorem 2.4 is met and we
have

In Theorem 2.7, we will give necessary and sufficient
probabilistic condition for the geometry of Banach space associated with the
above-mentioned strong law. In Theorem 2.12 we will replace geometric condition of Theorem
2.4 mentioned by probabilistic one to obtain SLLN (2.9).

Theorem 2.7. *Let , .
The following conditions are equivalent: *

(i)* is R-type p.
*(ii)* For every there exists such that for any independent, -valued, zero-mean random vectors , **For , the theorem is due to Woyczyński [14].*

*Proof. *(i)(ii) Using Chebyshev inequality ,Lemma 2.3
and Hölder's inequality ,we
have

(ii)(i) Let and let be an arbitrary sequence of independent random
vectors in ,
with and .
SetThen Thus, (i) follows directly from
Theorem 3.1 of Woyczyński [14].

Combining Theorem 2.7 with the result of Rosalsky and Van Thanh [18, Theorem 3.1], we get the following corollary.

Corollary 2.8. *Let , and let be a separable Banach space. If is family of independent, -valued, zero-mean random vectors, then the
following conditions are equivalent.*

(i)* for every and , there exists such that for any vectors , *(ii)*For every random vectors , the condition**
implies that
the SLLN holds.*

Before we state the next theorem, we need more notations and present some useful lemmas. Let and , where is defined as 0 and

Lemma (Fazekas [2, Lemma 2.5]). * Let be independent symmetric -valued random vectors. Assume that for all ,**then SLLN (2.9) holds.*

Lemma (Fazekas [2, Lemma 2.3] with ). *
Let be a field of independent, symmetric, -valued random vectors. If**then .*

Lemma 2.11. *
For , there exists a positive constant such that for any separable Banach space and any finite set of independent -valued random vectors with for all ,
the following holds.**For , **if , then it is possible to take **For , *

*Proof. *For the result is due to de Acosta [19, Theorem 2.1].
Since are independent the theorem is true in the
multidimensional case.

Theorem 2.12. *Let be a family of independent -valued zero-mean, random vectors and assume strongly in probability. Then*

(i)* if , then implies SLLN (2.9),*(ii)*if , then implies SLLN (2.9).*

Theorem 2.12 is multiple sum analogue of a strong law of large numbers, Theorem 3.2 of de Acosta [19].

*Proof. * (i)
Let us assume that are symmetric (desymmetryzation is standard)
and putBy assumption, it follows that and by the Borell-Cantelli lemma, it is enough
to prove It follows from assumption thatthus by Lemma 2.10and on the virtue of Lemma 2.9 and
the Borell-Cantelli lemma, the proof will be completed if we show that for any , Now, for any by Chebyshev inequality and Lemma 2.11, we
have

(ii) The same arguments and Hölder's inequality

#### Acknowledgment

The author is grateful to the referees for carefully reading the manuscript and valuable comments which help make this paper more clear and led to essential improvement of the first version.

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#### Copyright

Copyright © 2009 Zbigniew A. Lagodowski. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.