Research article

Note on complete convergence and complete moment convergence for negatively dependent random variables under sub-linear expectations

  • Received: 12 November 2022 Revised: 18 January 2023 Accepted: 28 January 2023 Published: 06 February 2023
  • MSC : 60F15, 60F05

  • In this article, we study the complete convergence and the complete moment convergence for negatively dependent (ND) random variables under sub-linear expectations. Under proper conditions of the moment of random variables, we establish the complete convergence and the complete moment convergence. As applications, we obtain the Marcinkiewcz-Zygmund type strong law of large numbers of ND random variables under sub-linear expectations. The results here generalize the corresponding ones in classic probability space to those under sub-linear expectations.

    Citation: Mingzhou Xu, Xuhang Kong. Note on complete convergence and complete moment convergence for negatively dependent random variables under sub-linear expectations[J]. AIMS Mathematics, 2023, 8(4): 8504-8521. doi: 10.3934/math.2023428

    Related Papers:

    [1] Lunyi Liu, Qunying Wu . Complete integral convergence for weighted sums of negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2023, 8(9): 22319-22337. doi: 10.3934/math.20231138
    [2] Mingzhou Xu, Kun Cheng, Wangke Yu . Complete convergence for weighted sums of negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2022, 7(11): 19998-20019. doi: 10.3934/math.20221094
    [3] Mingzhou Xu . Complete convergence and complete moment convergence for maximal weighted sums of extended negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2023, 8(8): 19442-19460. doi: 10.3934/math.2023992
    [4] Shuyan Li, Qunying Wu . Complete integration convergence for arrays of rowwise extended negatively dependent random variables under the sub-linear expectations. AIMS Mathematics, 2021, 6(11): 12166-12181. doi: 10.3934/math.2021706
    [5] He Dong, Xili Tan, Yong Zhang . Complete convergence and complete integration convergence for weighted sums of arrays of rowwise $ m $-END under sub-linear expectations space. AIMS Mathematics, 2023, 8(3): 6705-6724. doi: 10.3934/math.2023340
    [6] Mingzhou Xu . On the complete moment convergence of moving average processes generated by negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2024, 9(2): 3369-3385. doi: 10.3934/math.2024165
    [7] Chengcheng Jia, Qunying Wu . Complete convergence and complete integral convergence for weighted sums of widely acceptable random variables under the sub-linear expectations. AIMS Mathematics, 2022, 7(5): 8430-8448. doi: 10.3934/math.2022470
    [8] Mingzhou Xu . Complete convergence of moving average processes produced by negatively dependent random variables under sub-linear expectations. AIMS Mathematics, 2023, 8(7): 17067-17080. doi: 10.3934/math.2023871
    [9] Xiaocong Chen, Qunying Wu . Complete convergence and complete integral convergence of partial sums for moving average process under sub-linear expectations. AIMS Mathematics, 2022, 7(6): 9694-9715. doi: 10.3934/math.2022540
    [10] Haiwu Huang, Yuan Yuan, Hongguo Zeng . An extension on the rate of complete moment convergence for weighted sums of weakly dependent random variables. AIMS Mathematics, 2023, 8(1): 622-632. doi: 10.3934/math.2023029
  • In this article, we study the complete convergence and the complete moment convergence for negatively dependent (ND) random variables under sub-linear expectations. Under proper conditions of the moment of random variables, we establish the complete convergence and the complete moment convergence. As applications, we obtain the Marcinkiewcz-Zygmund type strong law of large numbers of ND random variables under sub-linear expectations. The results here generalize the corresponding ones in classic probability space to those under sub-linear expectations.



    Peng [1,2] introduced seminal concepts of the sub-linear expectations space to study the uncertainty in probability. The works of Peng [1,2] stimulate many scholars to investigate the results under sub-linear expectations space, extending those in classic probability space. Zhang [3,4] got exponential inequalities and Rosenthal's inequality under sub-linear expectations. For more limit theorems under sub-linear expectations, the readers could refer to Zhang [5], Xu and Zhang [6,7], Wu and Jiang [8], Zhang and Lin [9], Zhong and Wu [10], Chen [11], Chen and Wu [12], Zhang [13], Hu et al. [14], Gao and Xu [15], Kuczmaszewska [16], Xu and Cheng [17,18,19], Xu et al. [20] and references therein.

    In probability space, Shen et al. [21] obtained equivalent conditions of complete convergence and complete moment convergence for extended negatively dependent random variables. For references on complete moment convergence and complete convergence in probability space, the reader could refer to Hsu and Robbins [22], Chow [23], Ko [24], Meng et al. [25], Hosseini and Nezakati [26], Meng et al. [27] and refercences therein. Inspired by the work of Shen et al. [21], we try to investigate complete convergence and complete moment convergence for negatively dependent (ND) random variables under sub-linear expectations, and the Marcinkiewicz-Zygmund type result for ND random variables under sub-linear expectations, which complements the relevant results in Shen et al. [21].

    Recently, Srivastava et al. [28] introduced and studied comcept of statistical probability convergence. Srivastava et al. [29] investigated the relevant results of statistical probability convergence via deferred Nörlund summability mean. For more recent works, the interested reader could refer to Srivastava et al. [30,31,32], Paikary et al. [33] and references therein. We conjecture the relevant notions and results of statistical probability convergence could be extended to that under sub-linear expectation.

    We organize the remainders of this article as follows. We cite relevant basic notions, concepts and properties, and present relevant lemmas under sub-linear expectations in Section 2. In Section 3, we give our main results, Theorems 3.1 and 3.2, the proofs of which are given in Section 4.

    In this article, we use notions as in the works by Peng [2], Zhang [4]. Suppose that (Ω,F) is a given measurable space. Assume that H is a collection of all random variables on (Ω,F) satisfying φ(X1,,Xn)H for X1,,XnH, and each φCl,Lip(Rn), where Cl,Lip(Rn) represents the space of φ fulfilling

    |φ(x)φ(y)|C(1+|x|m+|y|m)(|xy|),x,yRn

    for some C>0, mN relying on φ.

    Definition 2.1. A sub-linear expectation E on H is a functional E:HˉR:=[,] fulfilling the following: for every X,YH,

    (a) XY yields E[X]E[Y];

    (b) E[c]=c, cR;

    (c) E[λX]=λE[X], λ0;

    (d) E[X+Y]E[X]+E[Y] whenever E[X]+E[Y] is not of the form or +.

    We name a set function V:F[0,1] a capacity if

    (a)V()=0, V(Ω)=1;

    (b)V(A)V(B), AB, A,BF.

    Moreover, if V is continuous, then V obey

    (c) AnA concludes V(An)V(A);

    (d) AnA concludes V(An)V(A).

    V is named to be sub-additive when V(A+B)V(A)+V(B), A,BF.

    Under the sub-linear expectation space (Ω,H,E), set V(A):=inf{E[ξ]:IAξ,ξH}, AF (cf. Zhang [3,4,9,13], Chen and Wu [12], Xu et al. [20]). V is a sub-additive capacity. Set

    V(A)=inf{n=1V(An):An=1An},AF.

    By Definition 4.2 and Lemma 4.3 of Zhang [34], if E=E is linear expectation, V coincide with the probability measure introduced by the linear expectation E. As in Zhang [3], V is countably sub-additive, V(A)V(A). Hence, in Theorem 3.1, Corollary 3.1, V could be replaced by V, implying that the results here could be considered as natural extensions of the corresponding ones in classic probability space. Write

    CV(X):=0V(X>x)dx+0(V(X>x)1)dx.

    Assume X=(X1,,Xm), XiH and Y=(Y1,,Yn), YiH are two random vectors on (Ω,H,E). Y is called to be negatively dependent to X, if for ψ1 on Cl,Lip(Rm), ψ2 on Cl,Lip(Rn), we have E[ψ1(X)ψ2(Y)]E[ψ1(X)]E[ψ2(Y)] whenever ψ1(X)0, E[ψ2(Y)]0, E[|ψ1(X)ψ2(Y)|]<, E[|ψ1(X)|]<, E[|ψ2(Y)|]<, and either ψ1 and ψ2 are coordinatewise nondecreasing or ψ1 and ψ2 are coordinatewise nonincreasing (cf. Definition 2.3 of Zhang [3], Definition 1.5 of Zhang [4]).

    {Xn}n=1 is called to be negatively dependent, if Xn+1 is negatively dependent to (X1,,Xn) for each n1. The existence of negatively dependent random variables {Xn}n=1 under sub-linear expectations could be yielded by Example 1.6 of Zhang [4] and Kolmogorov's existence theorem in classic probabililty space. We below give an concrete example.

    Example 2.1. Let P={Q1,Q2} be a family of probability measures on (Ω,F). Suppose that {Xn}n=1 are independent, identically distributed under each Qi, i=1,2 with Q1(X1=1)=Q1(X1=1)=1/2, Q2(X1=1)=1. Define E[ξ]=supQPEQ[ξ], for each random variable ξ. Here E[] is a sub-linear expectation. By the discussion of Example 1.6 of Zhang [4], we see that {Xn}n=1 are negatively dependent random variables under E.

    Assume that X1 and X2 are two n-dimensional random vectors in sub-linear expectation spaces (Ω1,H1,E1) and (Ω2,H2,E2) repectively. They are named identically distributed if for every ψCl,Lip(Rn),

    E1[ψ(X1)]=E2[ψ(X2)]. 

    {Xn}n=1 is called to be identically distributed if for every i1, Xi and X1 are identically distributed.

    In this article we assume that E is countably sub-additive, i.e., E(X)n=1E(Xn) could be implied by Xn=1Xn, X,XnH, and X0, Xn0, n=1,2,. Write Sn=ni=1Xi, n1. Let C denote a positive constant which may vary in different occasions. I(A) or IA represent the indicator function of A. The notion axbx means that there exist two positive constants C1, C2 such that C1|bx||ax|C2|bx|.

    As in Zhang [4], by definition, if X1,X2,,Xn are negatively dependent random variables and f1, f2,,fn are all non increasing (or non decreasing) functions, then f1(X1), f2(X2),,fn(Xn) are still negatively dependent random variables.

    We cite the useful inequalities under sub-linear expectations.

    Lemma 2.1. (See Lemma 4.5 (III) of Zhang [3]) If E is countably sub-additive under (Ω,H,E), then for XH,

    E|X|CV(|X|).

    Lemma 2.2. (See Lemmas 2.3, 2.4 of Xu et al. [20] and Theorem 2.1 of Zhang [4]) Assume that p1 and {Xn;n1} is a sequence of negatively dependent random varables under (Ω,H,E). Then there exists a positive constant C=C(p) relying on p such that

    E[|nj=1Xj|p]C{ni=1E|Xi|p+(ni=1[|E(Xi)|+|E(Xi)|])p},1p2, (2.1)
    E[max1in|ij=1Xj|p]C(logn)p{ni=1E|Xi|p+(ni=1[|E(Xi)|+|E(Xi)|])p},1p2, (2.2)
    E[max1in|ij=1Xi|p]C{ni=1E|Xi|p+(ni=1EX2i)p/2+(ni=1[|E(Xi)|+|E(Xi)|])p},p2. (2.3)

    Lemma 2.3. Assume that XH, α>0, γ>0, CV(|X|α)<. Then there exists a positive constant C relying on α,γ such that

    0V{|X|>γy}yα1dyCCV(|X|α)<.

    Proof. By the method of substitution of definite integral, letting γy=z1/α, we get

    0V{|X|>γy}yα1dy0V{|X|α>z}(z1/α/γ)α1z1/α1/γdzCCV(|X|α)<.

    Lemma 2.4. Let Yn,ZnH. Then for any q>1, ε>0 and a>0,

    E(max1jn|ji=1(Yi+Zi)|εa)+(1εq+1q1)1aq1E(max1jn|ji=1Yi|q)+E(max1jn|ji=1Zi|). (2.4)

    Proof. By Markov' inequality under sub-linear expectations, Lemma 2.1, and the similar proof of Lemma 2.4 of Sung [35], we could finish the proof. Hence, the proof is omitted here.

    Our main results are below.

    Theorem 3.1. Suppose α>12 and αp>1. Assume that {Xn,n1} is a sequence of negatively dependent random variables, and for each n1, Xn is identically distributed as X under sub-linear expectation space (Ω,H,E). Moreover, assume E(X)=E(X)=0 if p1. Suppose CV(|X|p)<. Then for all ε>0,

    n=1nαp2V{max1jn|ji=1Xi|>εnα}<. (3.1)

    Remark 3.1. By Example 2.1, the assumption E(X)=E(X)=0 if p1 in Theorem 3.1 can not be weakened to E(X)=0 if p1. In fact, in the case of Example 2.1, if 12<α1, αp>1, then for any 0<ε<1,

    n=1nαp2V{max1jn|ji=1Xi|>εnα}n=1nαp2V{max1jn|ji=1Xi|n}=n=1nαp2=+,

    which implies that Theorems 3.1, 3.2, Corollary 3.1 do not hold. However, by Example 1.6 of Zhang [4], the assumptions of Theorem 3.3, Corollary 3.2 hold for random variables in Example 2.1, hence Theorem 3.3, Corollary 3.2 are valid in this example.

    By Theorem 3.1, we could get the Marcinkiewicz-Zygmund strong law of large numbers for negatively dependent random variables under sub-linear expectations below.

    Corollary 3.1. Let α>12 and αp>1. Assume that under sub-linear expectation space (Ω,H,E), {Xn} is a sequence of negatively dependent random variables and for each n, Xn is identically distributed as X. Moreover, assume E(X)=E(X)=0 if p1. Assume that V induced by E is countably sub-additive. Suppose CV{|X|p}<. Then

    V(lim supn1nα|ni=1Xi|>0)=0. (3.2)

    Theorem 3.2. If the assumptions of Theorem 3.1 hold for p1 and CV{|X|plogθ|X|}< for some θ>max{αp1α12,p}, then for any ε>0,

    n=1nαp2αE(max1jn|ji=1Xi|εnα)+<. (3.3)

    By the similar proof of Theorem 3.1, with Theorem 2.1 (b) for negative dependent random variables of Zhang [4] (cf. the proof of Theorem 2.1 (c) there) in place of Lemma 2.2 here, we could obtain the following result.

    Theorem 3.3. Suppose α>12, p1, and αp>1. Assume that Xk is negatively dependent to (Xk+1,,Xn), for each k=1,,n, n1. Suppose for each n, Xn is identically distributed as X under sub-linear expectation space (Ω,H,E). Suppose CV(|X|p)<. Then for all ε>0,

    n=1nαp2V{max1jnji=1[XiE(Xi)]>εnα}<,
    n=1nαp2V{max1jnji=1[XiE(Xi)]>εnα}<.

    By the similar proof of Corollary 3.1, with Theorem 3.3 in place of Theorem 3.1, we get the following result.

    Corollary 3.2. Let α>12, p1, and αp>1. Assume that Xk is negatively dependent to (Xk+1,,Xn), for each k=1,,n, n1. Suppose for each n, Xn is identically distributed as X under sub-linear expectation space (Ω,H,E). Assume that V induced by E is countably sub-additive. Suppose CV{|X|p}<. Then

    V({lim supn1nαni=1[XiE(Xi)]>0}{lim supn1nαni=1[XiE(Xi)]>0})=0.

    By the similar proof of Theorem 3.1 and Corollary 3.1, and adapting the proof of (4.10), we could obtain the following result.

    Corollary 3.3. Suppose α>1 and p1. Assume that {Xn,n1} is a sequence of negatively dependent random variables, and for each n1, Xn is identically distributed as X under sub-linear expectation space (Ω,H,E). Suppose CV(|X|p)<. Then for all ε>0,

    n=1nαp2V{max1jnji=1[XiE(Xi)]>εnα}<,
    n=1nαp2V{max1jnji=1[XiE(Xi)]>εnα}<.

    Moreover assume that V induced by E is countably sub-additive. Then

    V({lim supn1nαni=1[XiE(Xi)]>0}{lim supn1nαni=1[XiE(Xi)]>0})=0.

    By the discussion below Definition 4.1 of Zhang [34], and Corollary 3.2, we conjecture the following.

    Conjecture 3.1. Suppose 12<α1 and αp>1. Assume that {Xn,n1} is a sequence of negatively dependent random variables, and for each n1, Xn is identically distributed as X under sub-linear expectation space (Ω,H,E). Assume that V induced by E is continuous. Suppose CV{|X|p}<. Then

    V({lim supn1nαni=1[XiE(Xi)]>0}{lim supn1nαni=1[XiE(Xi)]>0})=0.

    Proof of Theorem 3.1. We investigate the following cases.

    Case 1. 0<p<1.

    For fixed n1, for 1in, write

    Yni=nαI{Xi<nα}+XiI{|Xi|nα}+nαI{Xi>nα},
    Zni=(Xinα)I{Xi>nα}+(Xi+nα)I{Xi<nα},
    Yn=nαI{X<nα}+XI{|X|nα}+nαI{X>nα},
    Zn=XYn.

    Observing that Xi=Yni+Zni, we see that for all ε>0,

    n=1nαp2V{max1jn|ji=1Xi|>εnα}n=1nαp2V{max1jn|ji=1Yni|>εnα/2}+n=1nαp2V{max1jn|ji=1Zni|>εnα/2}=:I1+I2. (4.1)

    By Markov's inequality under sub-linear expectations, Cr inequality, and Lemmas 2.1, 2.3, we conclude that

    I1Cn=1nαp2αni=1E|Yni|=Cn=1nαp1αE|Yn|Cn=1nαp1αCV(|Yn|)Cn=1nαp1αnα0V{|Yn|>x}dx=Cn=1nαp1αnk=1kα(k1)αV{|X|>x}dx=Ck=1kα(k1)αV{|X|>x}dxn=knαp1α=Ck=1kα1V{|X|>(k1)α}kαpαCk=1kαp1V{|X|>kα}+CC0xαp1V{|X|>xα}dx+CCCV{|X|p}+C<, (4.2)

    and

    I2Cn=1nαp2αp/2ni=1E|Zni|p/2Cn=1nαp/21E|Zn|p/2Cn=1nαp/21CV{|Zn|p/2}Cn=1nαp/21CV{|X|p/2I{|X|>nα}}Cn=1nαp/21[nα0V{|X|>nα}sp/21ds+nαV{|X|>s}sp/21ds]Cn=1nαp1V{|X|>nα}+Cn=1nαp/21k=n(k+1)αkαV{|X|>s}sp/21dsCCV{|X|p}+Ck=1(k+1)αkαV{|X|>s}sp/21dskn=1nαp/21CCV{|X|p}+Ck=1V{|X|>kα}kαp1CCV{|X|p}+CCV{|X|p}<. (4.3)

    Therefore, by (4.1)–(4.3), we deduce that (3.1) holds.

    Case 2. p1.

    Observing that αp>1, we choose a suitable q such that 1αp<q<1. For fixed n1, for 1in, write

    X(1)ni=nαqI{Xi<nαq}+XiI{|Xi|nαq}+nαqI(Xi>nαq),
    X(2)ni=(Xinαq)I{Xi>nαq},X(3)ni=(Xi+nαq)I{Xi<nαq},

    and X(1)n, X(2)n, X(3)n is defined as X(1)ni, X(2)ni, X(3)ni only with X in place of Xi above. Observing that ji=1Xi=ji=1X(1)ni+ji=1X(2)ni+ji=1X(3)ni, for 1jn, we see that for all ε>0,

    n=1nαp2V{max1jn|ji=1Xi|>εnα}n=1nαp2V{max1jn|ji=1X(1)ni|>εnα/3}+n=1nαp2V{max1jn|ji=1X(2)ni|>εnα/3}+n=1nαp2V{max1jn|ji=1X(3)ni|>εnα/3}=:II1+II2+II3. (4.4)

    Therefore, to establish (3.1), it is enough to prove that II1<, II2<, II3<.

    For II1, we first establish that

    nαmax1jn|ji=1EX(1)ni|0, as n. (4.5)

    By E(X)=0, Markov's inequality under sub-linear expectations, Lemma 2.1, we conclude that

    nαmax1jn|ji=1EX(1)ni|nαni=1|E(X(1)n)|n1α|E(X(1)n)E(X)|n1αE|X(1)nX|n1αCV(|X(1)nX|)Cn1α[0V{|X|I{|X|>nαq}>x}dx]Cn1α[nαq0V{|X|>nαq}dx+nαqV{|X|>y}dy]Cn1α+αqV{|X|>nαq}+Cn1αnαqV{|X|>y}yp1nαq(p1)dyCn1α+αqE|X|pnαqp+Cn1α+αqαqpCV{|X|p}Cn1αqpα+αqCV{|X|p},

    which results in (4.5) by CV{|X|p}< and 1/(αp)<q<1. Thus, from (4.5), it follows that

    II1Cn=1nαp2V{max1jn|ji=1(X(1)niEX(1)ni)|>εnα6}. (4.6)

    For fixed n1, we note that {X(1)niEX(1)ni,1in} are negatively dependent random variables. By (4.6), Markov's inequality under sub-linear expectations, and Lemma 2.2, we see that for any β2,

    II1Cn=1nαp2αβE(max1jn|ji=1(X(1)niEX(1)ni)|β)Cn=1nαp2αβ[ni=1E|X(1)ni|β+(ni=1E|X(1)ni|2)β/2+(ni=1[|EX(1)ni|+|E(X(1)ni)|])β]=:II11+II12+II13. (4.7)

    Taking β>max{αp1α1/2,2,p,αp1αqpαq+α1}, we obtain

    αpαβ+αqβαpq1=α(pβ)(1q)1<1,
    αp2αβ+β/2<1,

    and

    αp2αβ+βαq(p1)β<1.

    By Cr inequality, Markov's inequality under sub-linear expectations, Lemma 2.1, we see that

    II11Cn=1nαp2αβni=1E|X(1)ni|βCn=1nαp1αβE|X(1)n|βCn=1nαp1αβCV{|X(1)n|β}=Cn=1nαp1αβnαqβ0V{|X|β>x}dxCn=1nαp1αβnαq0V{|X|>x}xβ1dxCn=1nαp1αβnαq0V{|X|>x}xp1nαq(βp)dxCn=1nαp1αβ+αqβαqpCV{|X|p}<, (4.8)
    II12Cn=1nαp2αβ(ni=1E|X(1)n|2)β/2Cn=1nαp2αβ+β/2(CV{|X(1)n|2})β/2Cn=1nαp2αβ+β/2(nαq0V{|X|>x}xdx)β/2{Cn=1nαp2αβ+β/2(CV{|X|2})β/2, if p2;Cn=1nαp2αβ+β/2(nαq0V{|X|>x}xp1nαq(2p)dx)β/2, if 1p<2,{Cn=1nαp2αβ+β/2(CV{|X|2})β/2<, if p2;Cn=1n(αp1)(1β/2)1(CV(|X|p))β/2<, if 1p<2, (4.9)

    and

    II13Cn=1nαp2αβ(ni=1[|EX(1)n|+|E(X(1)n)|])βCn=1nαp2αβ+β(E|X(1)nX|)βCn=1nαp2αβ+β(E|X|p)βnαq(p1)βCn=1nαp2αβ+βαq(p1)β(CV(|X|p))β<. (4.10)

    Therefore, combining (4.7)–(4.10) results in II1<.

    Next, we will establish that II2<. Let gμ(x) be a non-increasing Lipschitz function such that I{xμ}gμ(x)I{x1}, μ(0,1). Obviously, I{x>μ}>1gμ(x)>I{x>1}. For fixed n1, for 1in, write

    X(4)ni=(Xinαq)I(nαq<Xinα+nαq)+nαI(Xi>nα+nαq),

    and

    X(4)n=(Xnαq)I(nαq<Xnα+nαq)+nαI(X>nα+nαq).

    We see that

    (max1jn|ij=1X(2)ni|>εnα3)(max1in|Xi|>nα)(max1jn|ij=1X(4)ni|>εnα3),

    which results in

    II2n=1nαp2ni=1V{|Xi|>nα}+n=1nαp2V{max1jn|ij=1X(4)ni|>εnα3}=:II21+II22. (4.11)

    By CV{|X|p}<, we conclude that

    II21Cn=1nαp2ni=1E[1gμ(|Xi|)]=Cn=1nαp1E[1gμ(|X|)]Cn=1nαp1V{|X|>μnα}C0xαp1V{|X|>μxα}dxCCV(|X|p)<. (4.12)

    Observing that 1αp<q<1, from the definition of X(2)ni, follows that

    nαmax1jn|ji=1EX(4)ni|Cn1αE|X(4)n|Cn1αCV(|X(4)n|)Cn1α[nαq0V{|X|I{|X|>nαq}>x}dx+nαqV{|X|>x}dx]Cn1α+αqE|X|pnαpq+Cn1αnαqV{|X|>x}xp1nαq(p1)dxCn1α+αqαpqCV{|X|p}0 as n. (4.13)

    By X(4)ni>0, (4.11)–(4.13), we see that

    II2Cn=1nαp2V{|ni=1[X(4)niEX(4)ni]|>εnα6}. (4.14)

    For fixed n1, we know that {X(4)niEX(4)ni,1in} are negatively dependent random variables under sub-linear expectations. By Markov's inequality under sub-linear expectations, Cr-inequality, Lemma 2.2, we obtain

    II2Cn=1nαp2αβE(|ni=1[X(4)niEX(4)ni]|β)Cn=1nαp2αβ[ni=1E|X(4)ni|β+(ni=1E(X(4)ni)2)β/2+(ni=1[|EX(4)ni|+|E(X(4)ni)|])β]=:II21+II22+II23. (4.15)

    By Cr inequality, Lemma 2.3, we have

    II21Cn=1nαp2αβni=1E|X(4)n|βCn=1nαp1αβCV{|X(4)n|β}Cn=1nαp1αβCV{|X|βI{nαq<Xnα+nαq}+nαqβI{X>nα+nαq}}Cn=1nαp1αβ2nα0V{|X|>x}xβ1dxCn=1nαp1αβnk=12kα2(k1)αV{|X|>x}xβ1dxCk=1V{|X|>2(k1)α}kαβ1n=knαp1αβCk=1V{|X|>2(k1)α}kαp1C0V{|X|>2xα}xαp1dxCCV{|X|p}<. (4.16)

    As in the proof of (4.9) and (4.16), we can deduce that II22<.

    By Lemma 2.1, we see that

    II23Cn=1nαp1αβnβ(E|X(4)n|)βCn=1nαp1αβ+β(E|X|pnαq(p1))βCn=1nαp1αβ+βαq(p1)(CV{|X|p})β<. (4.17)

    By (4.15)–(4.17), we deduce that II2<.

    As in the proof of II2<, we also can obtain II3<. Therefore, combining (4.5), II1<, II2<, and II3< results in (3.1). This finishes the proof.

    Proof of Corollary 3.1. By CV{|X|p}<, and Theorem 3.1, we deduce that for all ε>0,

    n=1nαp2V(max1jn|ji=1Xi|>εnα)<. (4.18)

    By (4.18), we conclude that for any ε>0,

    >n=1nαp2V(max1jn|ji=1Xi|>εnα)=k=02k+11n=2knαp2V(max1jn|ji=1Xi|>εnα){k=0(2k)αp22kV(max1j2k|ji=1Xi|>ε2(k+1)α), if αp2,k=0(2k+1)αp22kV(max1j2k|ji=1Xi|>ε2(k+1)α), if 1<αp<2,{k=0V(max1j2k|ji=1Xi|>ε2(k+1)α), if αp2,k=012V(max1j2k|ji=1Xi|>ε2(k+1)α), if 1<αp<2,

    which, combined with Borel-Cantell lemma under sub-linear expectations, yields that

    V(lim supnmax1j2k|ji=1Xi|2(k+1)α>0)=0. (4.19)

    For all positive integers n, a positive integer k satisfying 2k1n<2k, we see that

    nα|ni=1Xi|max2k1n2knα|ni=1Xi|22αmax1j2k|ji=1Xi|2(k+1)α,

    which yields (3.2). This completes the proof.

    Proof of Theorem 3.2. For fixed n1, for 1in, write

    Yni=nαI{Xi<nα}+XiI{|Xi|nα}+nαI{Xi>nα},
    Zni=XiYni=(Xinα)I{Xi>nα}+(Xi+nα)I{Xi<nα},

    and

    Yn=nαI{X<nα}+XI{|X|nα}+nαI{X>nα},
    Zn=XYn=(Xnα)I{X>nα}+(X+nα)I{X<nα}.

    From Lemma 2.4 follows that for any β>1,

    n=1nαp2αE(max1jn|ji=1Xi|εnα)+Cn=1nαp2αβE(max1jn|ji=1(YniEYni)|β)+n=1nαp2αE(max1jn|ji=1(ZniEZni)|)=:III1+III2. (4.20)

    Noticing that Zn(|X|nα)I(|X|>nα)|X|I(|X|>nα), by Lemma 2.3, we see that

    III2Cn=1nαp2αni=1E|Zni|Cn=1nαp1αE|Zn|Cn=1nαp1αCV{|Zn|}Cn=1nαp1αCV{|X|I(|X|>nα)}Cn=1nαp1α[nα0V{|X|>nα}dx+nαV{|X|>x}dx]Cn=1nαp1V{|X|>nα}+Cn=1nαp1αk=n(k+1)αkαV{|X|>x}dxCCV{|X|p}+Ck=1V{|X|>kα}kα1kn=1nαp1α{CCV{|X|p}+Ck=1V{|X|>kα}kα1log(k), if p=1,CCV{|X|p}+Ck=1V{|X|>kα}kαp1, if p>1,{CCV{|X|log|X|}<, if p=1,CCV{|X|p}<, if p>1. (4.21)

    Now, we will establish III1<. Observing that θ>p1, we can choose β=θ. We analysize the following two cases.

    Case 1. 1<θ2. From (2.2) of Lemma 2.2, Lemma 2.1, E(Y)=E(Y)=0, and Markov's inequality under sub-linear expectations follows that

    III1=Cn=1nαp2αθE(max1jn|ji=1(YniEYni)|θ)Cn=1nαp2αθlogθn[ni=1E|Yni|θ+(ni=1[|E(Yni)|+|E(Yni)|])θ]=Cn=1nαp2αθlogθn[ni=1E|Yn|θ+(ni=1[|E(Yn)|+|E(Yn)|])θ]Cn=1nαp1αθlogθnCV{|Yn|θ}+Cn=1nαp2αθ+θlogθn(E|YnX|)θCn=1nαp1αθlogθnnα0V{|X|>x}xθ1dx+Cn=1nαp2αθ+θlogθn(CV{|YnX|})θCn=1nαp1αθlogθnnk=1kα(k1)αV{|X|>x}xθ1dx+Cn=1nαp2αθ+θlogθn(CV{|X|I{|X|>nα}})θCk=1V{|X|>(k1)α}kαθ1n=knαp1αθ+Cn=1nαp2αθ+θlogθn(0V{|X|I{|X|>nα}>y}dy)θCk=1V{|X|>(k1)α}kαp1logθk+Cn=1nαp2αθ+θlogθn(nαE{|X|plogθ|X|}nαplogθn)θ+Cn=1nαp2αθ+θlogθn(nαV{|X|plogθ|X|>yplogθy}d(yplogθy)/(nα(p1)logθn))θC0V{|X|>xα}xαp1logθxdx+C(CV{|X|plogθ|X|})θ+Cn=1nαp2+θαpθlogθθ2n(CV{|X|plogθ|X|})θCCV{|X|plogθ|X|}+C(CV{|X|plogθ|X|})θ<. (4.22)

    Case 2. θ>2. Observe that θ>αp1α12, we conclude that αp2αθ+θ2<1. As in the proof of (4.22), by Lemma 2.2, and Cr inequality, we see that

    III1=Cn=1nαp2αθE(max1jn|ji=1(YniE(Yni))|θ)Cn=1nαp2αθ[ni=1E|Yni|θ+(ni=1E|Yni|2)θ/2+(ni=1[|E(Yni)|+|E(Yni)|])θ]=:III11+III12+III13. (4.23)

    By Lemma 2.3, we see that

    III11Cn=1nαp1αθE|Yn|θCn=1nαp1αθCV{|Yn|θ}Cn=1nαp1αθnα0V{|X|>x}xθ1dx=Cn=1nαp1αθnk=1kα(k1)αV{|X|>x}xθ1dxCk=1V{|X|>(k1)α}kαθ1n=knαp1αθCk=1V{|X|>(k1)α}kαp1C0V{|X|>xα}xαp1dxCCV{|X|p}<.

    By Lemma 2.1, we deduce that

    III12Cn=1nαp2αθ(ni=1E|Yn|2)θ/2{Cn=1nαp2αθ+θ/2(E|X|2)θ/2, if p2,Cn=1nαp2αθ+θ/2(E|X|pnα(2p))θ/2, if 1p<2,{CCV{|X|2}<, if p2,Cn=1nαp2+θ/2αpθ/2(CV{|X|p})θ/2<, if 1p<2.

    And the proof of III13< is similar to that of (4.22). This finishes the proof.

    We have obtained new results about complete convergence and complete moment convergence for maximum partial sums of negatively dependent random variables under sub-linear expectations. Results obtained in our article extend those for negatively dependent random variables under classical probability space, and Theorems 3.1, 3.2 here are different from Theorems 3.1, 3.2 of Xu et al. [20], and the former can not be deduced from the latter. Corollary 3.1 complements Theorem 3.1 in Zhang [9] in the case p2, Corollaries 3.2, 3.3 complement Theorem 3.3 in Zhang [4] in the case p>1 in some sense.

    This study was supported by Science and Technology Research Project of Jiangxi Provincial Department of Education of China (No. GJJ2201041), Academic Achievement Re-cultivation Project of Jingdezhen Ceramic University (No. 215/20506135), Doctoral Scientific Research Starting Foundation of Jingdezhen Ceramic University (No. 102/01003002031).

    All authors state no conflicts of interest in this article.



    [1] S. G. Peng, G-expectation, G-Brownian motion and related stochastic calculus of Itô type, In: Stochastic analysis and applications, Abel Symposia, Vol. 2, Springer, Berlin, Heidelberg, 2007. https://doi.org/10.1007/978-3-540-70847-6_25
    [2] S. G. Peng, Nonlinear expectations and stochastic calculus under uncertainty, 1 Ed., Berlin: Springer, 2019. https://doi.org/10.1007/978-3-662-59903-7
    [3] L. X. Zhang, Exponential inequalities under the sub-linear expectations with applications to laws of the iterated logarithm, Sci. China Math., 59 (2016), 2503–2526. https://doi.org/10.1007/s11425-016-0079-1 doi: 10.1007/s11425-016-0079-1
    [4] L. X. Zhang, Rosenthal's inequalities for independent and negatively dependent random variables under sub-linear expectations with applications, Sci. China Math., 59 (2016), 751–768. https://doi.org/10.1007/s11425-015-5105-2 doi: 10.1007/s11425-015-5105-2
    [5] L. X. Zhang, Donsker's invariance principle under the sub-linear expectation with an application to Chung's law of the iterated logarithm, Commun. Math. Stat., 3 (2015), 187–214. https://doi.org/10.1007/s40304-015-0055-0 doi: 10.1007/s40304-015-0055-0
    [6] J. P. Xu, L. X. Zhang, Three series theorem for independent random variables under sub-linear expectations with applications, Acta Math. Sin., English Ser., 35 (2019), 172–184. https://doi.org/10.1007/s10114-018-7508-9 doi: 10.1007/s10114-018-7508-9
    [7] J. P. Xu, L. X. Zhang, The law of logarithm for arrays of random variables under sub-linear expectations, Acta Math. Appl. Sin. English Ser., 36 (2020), 670–688. https://doi.org/10.1007/s10255-020-0958-8 doi: 10.1007/s10255-020-0958-8
    [8] Q. Y. Wu, Y. Y. Jiang, Strong law of large numbers and Chover's law of the iterated logarithm under sub-linear expectations, J. Math. Anal. Appl., 460 (2018), 252–270. https://doi.org/10.1016/j.jmaa.2017.11.053 doi: 10.1016/j.jmaa.2017.11.053
    [9] L. X. Zhang, J. H. Lin, Marcinkiewicz's strong law of large numbers for nonlinear expectations, Stat. Probab. Lett., 137 (2018), 269–276. https://doi.org/10.1016/j.spl.2018.01.022 doi: 10.1016/j.spl.2018.01.022
    [10] H. Y. Zhong, Q. Y. Wu, Complete convergence and complete moment convergence for weighted sums of extended negatively dependent random variables under sub-linear expectation, J. Inequal. Appl., 2017 (2017), 261. https://doi.org/10.1186/s13660-017-1538-1 doi: 10.1186/s13660-017-1538-1
    [11] Z. J. Chen, Strong laws of large numbers for sub-linear expectations, Sci. China Math., 59 (2016), 945–954. https://doi.org/10.1007/s11425-015-5095-0 doi: 10.1007/s11425-015-5095-0
    [12] X. C. Chen, Q. Y. Wu, Complete convergence and complete integral convergence of partial sums for moving average process under sub-linear expectations, AIMS Math., 7 (2022), 9694–9715. https://doi.org/10.3934/math.2022540 doi: 10.3934/math.2022540
    [13] L. X. Zhang, Strong limit theorems for extended independent and extended negatively dependent random variables under sub-linear expectations, Acta Math. Sci. English Ser., 42 (2022), 467–490. https://doi.org/10.1007/s10473-022-0203-z doi: 10.1007/s10473-022-0203-z
    [14] F. Hu, Z. J. Chen, D. F. Zhang, How big are the increments of G-Brownian motion, Sci. China Math., 57 (2014), 1687–1700. https://doi.org/10.1007/s11425-014-4816-0 doi: 10.1007/s11425-014-4816-0
    [15] F. Q. Gao, M. Z. Xu, Large deviations and moderate deviations for independent random variables under sublinear expectations, Sci. China Math., 41 (2011), 337–352. https://doi.org/10.1360/012009-879 doi: 10.1360/012009-879
    [16] A. Kuczmaszewska, Complete convergence for widely acceptable random variables under the sublinear expectations, J. Math. Anal. Appl., 484 (2020), 123662. https://doi.org/10.1016/j.jmaa.2019.123662 doi: 10.1016/j.jmaa.2019.123662
    [17] M. Z. Xu, K. Cheng, Convergence for sums of iid random variables under sublinear expectations, J. Inequal. Appl., 2021 (2021), 157. https://doi.org/10.1186/s13660-021-02692-x doi: 10.1186/s13660-021-02692-x
    [18] M. Z. Xu, K. Cheng, How small are the increments of G-Brownian motion, Stat. Probab. Lett., 186 (2022), 109464. https://doi.org/10.1016/j.spl.2022.109464 doi: 10.1016/j.spl.2022.109464
    [19] M. Z. Xu, K. Cheng, Note on precise asymptotics in the law of the iterated logarithm under sublinear expectations, Math. Probl. Eng., 2022 (2022), 6058563. https://doi.org/10.1155/2022/6058563 doi: 10.1155/2022/6058563
    [20] M. Z. Xu, K. Cheng, W. K. Yu, Complete convergence for weighted sums of negatively dependent random variables under sub-linear expectations, AIMS Math., 7 (2022), 19998–20019. https://doi.org/10.3934/math.20221094 doi: 10.3934/math.20221094
    [21] M. Yao, B. Q. Xiao, Equivalent conditions of complete convergence and complete moment convergence for END random variables, Chin. Ann. Math. Ser. B, 39 (2018), 83–96. https://doi.org/10.1007/s11401-018-1053-9 doi: 10.1007/s11401-018-1053-9
    [22] P. L. Hsu, H. Robbins, Complete convergence and the law of large numbers, Proc. Natl. Acad. Sci., 33 (1947), 25–31. https://doi.org/10.1073/pnas.33.2.25 doi: 10.1073/pnas.33.2.25
    [23] Y. S. Chow, On the rate of moment convergence of sample sums and extremes, Bull. Inst. Math. Acad. Sin., 16 (1988), 177–201.
    [24] M. H. Ko, Complete moment convergence of moving average process generated by a class of random variables, J. Inequal. Appl., 2015 (2015), 225. https://doi.org/10.1186/s13660-015-0745-x doi: 10.1186/s13660-015-0745-x
    [25] B. Meng, D. C. Wang, Q. Y. Wu, Convergence of asymptotically almost negatively associated random variables with random coefficients, Commun. Stat.-Theor. M., 2021 (2021), 1963457. https://doi.org/10.1080/03610926.2021.1963457 doi: 10.1080/03610926.2021.1963457
    [26] S. M. Hosseini, A. Nezakati, Complete moment convergence for the dependent linear processes with random coefficients, Acta Math. Sin., English Ser., 35 (2019), 1321–1333. https://doi.org/10.1007/s10114-019-8205-z doi: 10.1007/s10114-019-8205-z
    [27] B. Meng, D. C. Wang, Q. Y. Wu, Complete convergence and complete moment convergence for weighted sums of extended negatively dependent random variables, Commun. Stat.-Theor. M., 51 (2022), 3847–3863. https://doi.org/10.1080/03610926.2020.1804587 doi: 10.1080/03610926.2020.1804587
    [28] H. M. Srivastava, B. B. Jena, S. K. Paikray, A certain class of statistical probability convergence and its applications to approximation theorems, Appl. Anal. Discrete Math., 14 (2020), 579–598. https://doi.org/10.2298/AADM190220039S doi: 10.2298/AADM190220039S
    [29] H. M. Srivastava, B. B. Jena, S. K. Paikray, Statistical probability convergence via the deferred Nörlund mean and its applications to approximation theorems, RACSAM, 114 (2020), 1–14. https://doi.org/10.1007/s13398-020-00875-7 doi: 10.1007/s13398-020-00875-7
    [30] H. M. Srivastava, B. B. Jena, S. K. Paikray, U. K. Misra, Statistically and relatively modular deferred-weighted summability and Korovkin-type approximation theorems, Symmetry, 11 (2019), 1–20. https://doi.org/10.3390/sym11040448 doi: 10.3390/sym11040448
    [31] H. M. Srivastava, B. B. Jena, S. K. Paikray, Deferred Cesàro statistical probability convergence and its applications to approximation theorems, J. Nonlinear Convex Anal., 20 (2019), 1777–1792.
    [32] H. M. Srivastava, B. B. Jena, S. K. Paikray, Statistical deferred Nörlund summability and Korovkin-type approximation theorem, Mathematics, 8 (2020), 1–11. https://doi.org/10.3390/math8040636 doi: 10.3390/math8040636
    [33] S. K. Paikray, P. Parida, S. A. Mohiuddine, A certain class of relatively equi-statistical fuzzy approximation theorems, Eur. J. Pure Appl. Math., 13 (2020), 1212–1230. https://doi.org/10.29020/nybg.ejpam.v13i5.3711 doi: 10.29020/nybg.ejpam.v13i5.3711
    [34] L. X. Zhang, On the laws of the iterated logarithm under sub-linear expectations, Probab. Uncertain. Qua., 6 (2021), 409–460. https://doi.org/10.3934/puqr.2021020 doi: 10.3934/puqr.2021020
    [35] S. H. Sung, Moment inequalities and complete monent convergence, J. Inequal. Appl., 2009 (2009), 271265. https://doi.org/10.1155/2009/271265 doi: 10.1155/2009/271265
  • This article has been cited by:

    1. Mingzhou Xu, Complete convergence of moving average processes produced by negatively dependent random variables under sub-linear expectations, 2023, 8, 2473-6988, 17067, 10.3934/math.2023871
    2. Mingzhou Xu, Xuhang Kong, Complete qth moment convergence of moving average processes for m-widely acceptable random variables under sub-linear expectations, 2024, 214, 01677152, 110203, 10.1016/j.spl.2024.110203
    3. Mingzhou Xu, Kun Cheng, Wangke Yu, Convergence of linear processes generated by negatively dependent random variables under sub-linear expectations, 2023, 2023, 1029-242X, 10.1186/s13660-023-02990-6
    4. Mingzhou Xu, On the complete moment convergence of moving average processes generated by negatively dependent random variables under sub-linear expectations, 2024, 9, 2473-6988, 3369, 10.3934/math.2024165
    5. Mingzhou Xu, Complete convergence and complete moment convergence for maximal weighted sums of extended negatively dependent random variables under sub-linear expectations, 2023, 8, 2473-6988, 19442, 10.3934/math.2023992
    6. Lunyi Liu, Qunying Wu, Complete integral convergence for weighted sums of negatively dependent random variables under sub-linear expectations, 2023, 8, 2473-6988, 22319, 10.3934/math.20231138
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1354) PDF downloads(52) Cited by(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog