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An extension of the Baum-Katz theorem to i.i.d. random variables with general moment conditions
Journal of Inequalities and Applications volume 2015, Article number: 414 (2015)
Abstract
For a sequence of i.i.d. random variables \(\{X, X_{n}, n\ge1\}\) and a sequence of positive real numbers \(\{a_{n}, n\ge1\}\) with \(0< a_{n}/n^{1/p}\uparrow\) for some \(0< p<2\), the Baum-Katz complete convergence theorem is extended to the \(\{X, X_{n}, n\ge1\}\) with the general moment condition \(\sum^{\infty}_{n=1}n^{r-1}P\{|X|>a_{n}\}<\infty\), where \(r\ge1\). The relationship between the complete convergence and the strong law of large numbers is established.
1 Introduction and main result
The concept of complete convergence was first introduced by Hsu and Robbins [1] and has played a very important role in probability theory. A sequence of random variables \(\{U_{n},n\geq1\}\) is said to converge completely to a constant C if \(\sum^{\infty}_{n=1}P\{|U_{n}-C|>\varepsilon\}<\infty\) for any \(\varepsilon>0\). Hsu and Robbins [1] proved that the sequence of arithmetic means of independent and identically distributed (i.i.d.) random variables converges completely to the expected value if the variance of the summands is finite. Their result has been generalized and extended by many authors.
The following result is well known.
Theorem A
Let \(r\geq1\) and \(0< p<2\). Let \(\{X, X_{n},n\geq1\}\) be a sequence of i.i.d. random variables with partial sums \(S_{n}=\sum_{k=1}^{n} X_{k}\), \(n\geq1\). Then the following statements are equivalent:
where \(b=0\) if \(0< rp<1\) and \(b=EX\) if \(rp\geq1\).
When \(r=1\), each of (1.1)∼(1.3) is equivalent to
When \(r=1\), the equivalence of (1.1) and (1.4) is known as the Marcinkiewicz and Zygmund strong law of large numbers. Katz [2] proved the equivalence of (1.1) and (1.2) for the case of \(p=1\). Baum and Katz [3] proved the equivalence of (1.1) and (1.2) for the case of \(0< p<2\). The result of Baum and Katz was generalized and extended in several directions. Some versions of the Baum and Katz theorem under higher-order moment conditions were established by Lanzinger [4], Gut and Stadtmüller [5], and Chen and Sung [6]. When \(p=1\), \(1\leq r<3\), and \(\{X_{n}, n\geq1\}\) is a sequence of pairwise independent, but not necessarily identically distributed, random variables, Spătaru [7] gave sufficient conditions for (1.2).
It is interesting to find more general moment conditions such that the complete convergence holds. In fact, Li et al. [8] and Sung [9] have done something. In particular, it is worth pointing out that Sung [9] obtained the following complete convergence for pairwise i.i.d. random variables \(\{X,X_{n},n\geq1\}\):
provided that \(\sum^{\infty}_{n=1}P\{|X|>a_{n}\}<\infty\), where \(0< a_{n}/n\uparrow\).
Motivated by the work of Sung [9], the aim of this paper is to obtain the complete convergence under more general moment conditions. Our main result includes the Baum and Katz [3] complete convergence and the Marcinkiewicz and Zygmund strong law of large numbers.
Now we state the main result. Some lemmas and the proof of the main result will be detailed in next section.
Theorem 1.1
Let \(r\geq1\) and \(0< p<2\). Let \(\{X, X_{n},n\geq1\}\) be a sequence of i.i.d. random variables with partial sums \(S_{n}=\sum_{k=1}^{n} X_{k}\), \(n\geq1\), and \(\{a_{n},n\geq1\}\) a sequence of positive real numbers with \(0< a_{n}/n^{1/p}\uparrow\). Then the following statements are equivalent:
where \(b_{n}=0\) if \(0< p<1\) and \(b_{n}=EXI(|X|\leq a_{n})\) if \(1\leq p<2\).
When \(r=1\), each of (1.5)-(1.7) is equivalent to
Remark 1.1
When \(a_{n}=n^{1/p}\) for \(n\geq1\), (1.5) is equivalent to (1.1). In this case, \(a_{n}^{-1}\cdot n EXI(|X|>a_{n})\to0\) if \(1\leq p<2\) and (1.1) holds. Hence, (1.1) ⇒ (1.2), (1.1) ⇒ (1.3), and (1.1) ⇒ (1.4) (in this case, \(r=1\)) follow from Theorem 1.1. Although the converses do not follow directly from Theorem 1.1, the proofs can be done easily. When \(a_{n}=n^{1/p}(\ln n)^{\alpha}\) for \(n\geq1\), where \(\alpha>0\), (1.5) is equivalent to \(E|X|^{rp}/(\ln(|X|+2))^{\alpha rp}<\infty\).
Throughout this paper, the symbol C denotes a positive constant that is not necessarily the same one in each appearance, and \(I(A)\) denotes the indicator function of an event A.
2 Lemmas and proofs
To prove the main result, the following lemmas are needed. Lemma 2.1 is the Rosenthal inequality for the sum of independent random variables; see, for example, Petrov [10].
Lemma 2.1
Let \(\{Y_{n}, n\ge1 \}\) be a sequence of independent random variables with \(EY_{n}=0\) and \(E|Y_{n}|^{s}<\infty\) for some \(s\ge2\) and all \(n\ge1\). Then there exists a positive constant C depending only on s such that for all \(n\ge1\),
Lemma 2.2
Under the assumptions of Theorem 1.1, if \(0< p<1\) and (1.5) holds, then
as \(n\rightarrow\infty\).
Proof
Since \(0< p<1\), by \(0< a_{n}/n^{1/p}\uparrow\) we have \(0< a_{n}/n\uparrow\infty\). By (1.5) we have
Therefore, by Lemma 2.4 in Sung [9] we have the desired result. □
Lemma 2.3
Under the assumptions of Theorem 1.1, if \(rp\geq2\) and (1.5) holds, then
Proof
By \(0< a_{n}/n^{1/p}\uparrow\) we have \(a_{k}/a_{n}\leq (k/n)^{1/p}\) for any \(1\leq k\leq n\). Hence,
Set \(C=1+ r2^{r-1}\sum^{\infty}_{k=1}k^{r-1}P\{|X|>a_{k}\}\). By (1.5), \(C<\infty\). So we complete the proof. □
Lemma 2.4
Under the assumptions of Theorem 1.1, if \(s>rp\) and (1.5) holds, then
Proof
By \(0< a_{n}/n^{1/p}\uparrow\) we have \(a_{k}/a_{n}\leq (k/n)^{1/p}\) for any \(n\geq k\). Hence,
Therefore, the proof is completed. □
Lemma 2.5
Let \(\{X,X_{n}\geq1\}\) be a sequence of i.i.d. symmetric random variables, and \(\{a_{n},n\geq1\}\) a sequence of real numbers with \(0< a_{n}\uparrow \infty\). Suppose that
Then
Proof
Set \(S_{n}=\sum^{n}_{k=1}X_{k}\), \(n\geq1\). Note that for all \(\varepsilon>0\),
and \(P\{|X|>\varepsilon a_{2n+1}/2 \}\rightarrow0\) as \(n\rightarrow \infty\). Hence, to prove (2.2), it suffices to prove that
We will prove (2.3) by contradiction. Suppose that there exist a constant \(\varepsilon>0\) and a sequence of integers \(\{n_{i},i\geq1\}\) with \(n_{i}\uparrow\infty\) such that
Without loss of generality, we can assume that \(2n_{i}< n_{i+1}\). By the Lévy inequality (see, for example, formula (2.6) in Ledoux and Talagrand [11]) we have
which leads a contradiction to (2.1). Hence, (2.3) holds, and so the proof is completed. □
Proof of Theorem 1.1
We first prove that (1.5) implies (1.7). By Lemma 2.2, to prove (1.7), it suffices to prove that
Note that
Hence, by (1.5), to prove (2.4) it suffices to prove that for all \(\varepsilon>0\),
For any \(s\geq2\), by the Markov inequality and Lemma 2.1,
If \(rp\geq2\), taking s large enough such that \(r-2-s/p+s/2<-1\), by Lemma 2.3 we have
Since \(s>rp\), \(I_{2}<\infty\) by Lemma 2.4. If \(0< rp<2\), taking \(s=2\) (in this case \(I_{1}=I_{2}\)), we have \(I_{1}=I_{2}<\infty\) by Lemma 2.4 again. Hence, (2.5) holds for all \(\varepsilon>0\).
It is trivial that (1.7) implies (1.6). Now we prove that (1.6) implies (1.5). Let \(\{X', X_{n}', n\geq1\}\) be an independent copy of \(\{X,X_{n},n\geq1\}\). Then we also have
Hence,
from which it follows that
Then, by Lemma 2.5,
By the Lévy inequality (see, for example, formula (2.7) in Ledoux and Talagrand [11]), for any fixed \(\varepsilon>0\),
as \(n\rightarrow\infty\). Then, for all n large enough,
Therefore, by Lemma 2.6 in Ledoux and Talagrand [11] and the Lévy inequality (see formula (2.7) in Ledoux and Talagrand [11]) we have that for all n large enough,
Therefore,
Since \(P\{|X|>a_{n}/2\}\to0\) as \(n\to\infty\), \(|\operatorname{med}(X)/(a_{n}/2)|\leq1\) for all n large enough. By the weak symmetrization inequality we have that for all n large enough,
which, together with (2.6), implies that (1.5) holds.
Finally, we prove that (1.5) and (1.8) are equivalent when \(r=1\). Assume that (1.5) holds for \(r=1\). Since \(\sum^{\infty}_{i=1}iP\{a_{i}<|X|\leq a_{i+1}\}=\sum^{\infty}_{n=1} P\{ |X|>a_{n}\}<\infty\), for any fixed \(\varepsilon>0\), there exists a positive integer N such that \(\sum^{\infty}_{i=N+1}iP\{a_{i}<|X|\leq a_{i+1}\}<\varepsilon\). Then, for \(n>N+1\),
It follows that
as \(n\to\infty\). Hence, to prove (1.8), by Lemma 2.2 it suffices to prove that
Since \(0< a_{n}/n^{1/p}\uparrow\) and \(0< p<2\),
Then by the Kolmogorov convergence criterion and the Kronecker lemma, (2.8) holds, and so (1.8) also holds.
Conversely, assume that (1.8) holds. Let \(\{X', X_{n}', n\geq1\}\) be an independent copy of \(\{X,X_{n},n\geq1\}\). Then we also have
Hence, we have
So, we have by \(0< a_{n}\uparrow\) that
By the Borel-Cantelli lemma,
which, together with (2.7), implies that (1.5) holds for \(r=1\). So we complete the proof. □
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Acknowledgements
The authors would like to thank the referees for the helpful comments. The research of Pingyan Chen and Jiaming Yi is supported by the National Natural Science Foundation of China (No. 11271161). The research of Soo Hak Sung is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2014R1A1A2058041).
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Chen, P., Yi, J. & Sung, S.H. An extension of the Baum-Katz theorem to i.i.d. random variables with general moment conditions. J Inequal Appl 2015, 414 (2015). https://doi.org/10.1186/s13660-015-0939-2
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DOI: https://doi.org/10.1186/s13660-015-0939-2