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Complete convergence and complete moment convergence for a class of random variables
Journal of Inequalities and Applications volume 2012, Article number: 229 (2012)
Abstract
In this paper, we establish the complete convergence and complete moment convergence and obtain the equivalence of the complete convergence and complete moment convergence for the class of random variables satisfying a Rosenthal-type maximal inequality. Baum-Katz-type theorem and Hsu-Robbins-type theorem are extended to the case of this class of random variables.
MSC:60F15.
1 Introduction
The concept of complete convergence was introduced by Hsu and Robbins [1] as follows. A sequence of random variables is said to converge completely to a constant C if for all . In view of the Borel-Cantelli lemma, this implies that almost surely (a.s.). The converse is true if the are independent. 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. Erdös [2] proved the converse. The result of Hsu-Robbin-Erdös is a fundamental theorem in probability theory which has been generalized and extended in several directions by many authors. One of the most important generalizations is that by Baum and Katz [3] for the strong law of large numbers as follows.
Theorem A (Baum and Katz [3])
Let and . Let be a sequence if i.i.d. random variables. Assume further that if . Then the following statements are equivalent:
Many authors studied the Baum-Katz-type theorem for dependent random variables; see, for example, Peligrad [4] for a strong stationary ρ-mixing sequence, Peligrad and Gut [5] for a -mixing sequence, Stoica [6, 7] for a martingale difference sequence, Stoica [8] for bounded subsequences, Wang and Hu [9] for φ-mixing random variables, and so forth.
One of the most interesting inequalities to probability theory is the Rosenthal-type maximal inequality. For a sequence of i.i.d. random variables with for some , there exist positive constants depending only on q such that
The inequality above has been obtained for dependent random variables by many authors. See, for example, Shao [10] for negatively associated random variables, Utev and Peligrad [11] for -mixing random variables, Wang et al. [12] for φ-mixing random variables with the mixing coefficients satisfying certain conditions, and so forth.
The purpose of this work is to obtain complete convergence and complete moment convergence for a sequence of random variables satisfying a Rosenthal-type maximal inequality.
Throughout the paper, let be a sequence of random variables defined on a fixed probability space . Let be the indicator function of the set A. Denote , , , (). The symbol C denotes a positive constant which may be different in various places.
The following definition will be used frequently in the paper.
Definition 1.1 A sequence of random variables is said to be stochastically dominated by a random variable X if there exists a positive constant C such that
for all and .
In this paper, the present investigation is to provide the complete convergence and complete moment convergence for a sequence of the class of random variables satisfying a Rosenthal-type maximal inequality and prove the equivalence of the complete convergence and complete moment convergence. Baum-Katz-type theorem and Hsu-Robbins-type theorem are extended to the case of the class of random variables satisfying a Rosenthal-type maximal inequality. As a result, the Marcinkiewicz-Zygmund strong law of large numbers for the class of random variables is obtained.
2 Main results
Theorem 2.1 Let , and . Suppose that is a sequence of zero mean random variables which is stochastically dominated by a random variable X with . Assume that for any , there exists a positive constant depending only on q such that
where for all . Then
and
Furthermore, (2.2) is equivalent to (2.3).
Corollary 2.1 Let . Suppose that is a sequence of zero mean random variables which is stochastically dominated by a random variable X with . Assume further that (2.1) holds, then
For , we have the following theorem.
Theorem 2.2 Let . Suppose that is a sequence of zero mean random variables which is stochastically dominated by a random variable X with . Assume further that (2.1) holds, then for all ,
Theorem 2.3 Let , and . Suppose that is a sequence of zero mean random variables which is stochastically dominated by a random variable X with . Assume further that (2.1) holds, then for all ,
Remark 2.1 In (2.1), is a monotone transformation of . If is a sequence of independent random variables, then (2.1) is clearly satisfied. There are many sequences of dependent random variables satisfying (2.1) for all . Examples include sequences of NA random variables (see Shao [10]), -mixing random variables (see Utev and Peligrad [11]), φ-mixing random variables with the mixing coefficients satisfying certain conditions (see Wang et al. [12]), -mixing random variables with the mixing coefficients satisfying certain conditions (see Wang and Lu [13]).
Remark 2.2 In Theorem 2.1, we not only generalize the Baum-Katz-type theorem for the class of random variables satisfying (2.1), but also consider the case . Furthermore, if we take and , then we can get the Hsu-Robbins-type theorem (see Hsu and Robbins [1]) for the class of random variables satisfying (2.1).
3 Lemmas
In this section, the following lemmas are very useful to prove the main results of the paper.
Lemma 3.1 (cf. Wu [14])
Let be a sequence of random variables, which is stochastically dominated by a random variable X. Then for any and , the following two statements hold:
and
where and are positive constants.
Lemma 3.2 Under the conditions of Theorem 2.1,
Proof For fixed , denote , . Then it follows that
For J, noting that , we have by Markov’s inequality, Lemma 3.1 and that
For I, by Markov’s inequality and (2.1), we have that for ,
We will consider the following three cases:
Case 1. , and .
Taking , which implies that . We have by Lemma 3.1 and the proof of (3.2) that
Note that if for . We have that
Case 2. , and .
Take . Similar to the proofs of (3.3) and (3.4), we have that
Case 3. , and .
Take . Note that if . Similar to the proofs of (3.5), it follows that . From the statements above, (3.1) is proved. The proof of the lemma is completed. □
Lemma 3.3 (cf. Sung [15])
Let and be sequences of random variables. Then for any , and ,
4 The proofs of main results
Proof of Theorem 2.1 First, we prove (2.2). For fixed , let and , . Then it is easy to have that
For , noting that , we have by Markov’s inequality, Lemma 3.1 and the proof of (3.2) that
For , by Markov’s inequality and (2.1), we have that for any ,
We consider the following three cases:
Case 1. , and .
Take , which implies that .
For , we have by ’s inequality, the proofs of (3.2) and (3.4) that
For , note that if for . We have that
Case 2. , and .
Take . Similar to the proofs of (4.2), (4.3), (3.5) and (4.1), we have that
Case 3. , and .
Take . Note that if . Similar to the proof of (4.4), it follows that . From all the statements above, we have proved (2.2).
Next, we prove (2.3). Since and , . By Lemma 3.3, the proofs of (4.1) and , it follows that
Hence (2.3) holds.
We will prove the equivalence of (2.2) and (2.3). First, we prove that (2.2) implies (2.3). In fact, for all , we have by Lemma 3.2 that
Next, we prove that (2.3) implies (2.2). It is easy to see that
Hence, by (4.5), (2.3) implies (2.2). The proof of the theorem is completed. □
Proof of Corollary 2.1 Taking in Theorem 2.1, we have that for all ,
The rest of the proof is similar to that of Theorem 3.1 in Dung and Tien [16] and is omitted. □
Proof of Theorem 2.2 We use the same notation as that in the proof of Theorem 2.1. Taking and in Lemma 3.3, by (2.1) and Lemma 3.1, it follows that
Hence, (2.4) holds. □
Proof of Theorem 2.3 Inspired by the proof of Theorem 12.1 of Gut [17], we have that
The desired result (2.5) follows from (2.2) and (4.6) immediately. □
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Acknowledgements
The authors are most grateful to the editor Andrei Volodin and anonymous referees for careful reading of the manuscript and valuable suggestions which helped in significantly improving an earlier version of this paper. The research was supported by the National Natural Science Foundation of China (11171001, 11201001, 11126176), Natural Science Foundation of Anhui Province (1208085QA03), Provincial Natural Science Research Project of Anhui Colleges (KJ2010A005), Academic Innovation Team of Anhui University (KJTD001B), Doctoral Research Start-up Funds Projects of Anhui University, and the Talents Youth Fund of Anhui Province Universities (2011SQRL012ZD).
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Wang, X., Hu, S. Complete convergence and complete moment convergence for a class of random variables. J Inequal Appl 2012, 229 (2012). https://doi.org/10.1186/1029-242X-2012-229
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DOI: https://doi.org/10.1186/1029-242X-2012-229