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Nonlinear autoregressive sieve bootstrap based on extreme learning machines

  • The aim of the paper is to propose and discuss a sieve bootstrap scheme based on Extreme Learning Machines for non linear time series. The procedure is fully nonparametric in its spirit and retains the conceptual simplicity of the residual bootstrap. Using Extreme Learning Machines in the resampling scheme can dramatically reduce the computational burden of the bootstrap procedure, with performances comparable to the NN-Sieve bootstrap and computing time similar to the ARSieve bootstrap. A Monte Carlo simulation experiment has been implemented, in order to evaluate the performance of the proposed procedure and to compare it with the NN-Sieve bootstrap. The distributions of the bootstrap variance estimators appear to be consistent, delivering good results both in terms of accuracy and bias, for either linear and nonlinear statistics (such as the mean and the median) and smooth functions of means (such as the variance and the covariance).

    Citation: Michele La Rocca, Cira Perna. Nonlinear autoregressive sieve bootstrap based on extreme learning machines[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 636-653. doi: 10.3934/mbe.2020033

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  • The aim of the paper is to propose and discuss a sieve bootstrap scheme based on Extreme Learning Machines for non linear time series. The procedure is fully nonparametric in its spirit and retains the conceptual simplicity of the residual bootstrap. Using Extreme Learning Machines in the resampling scheme can dramatically reduce the computational burden of the bootstrap procedure, with performances comparable to the NN-Sieve bootstrap and computing time similar to the ARSieve bootstrap. A Monte Carlo simulation experiment has been implemented, in order to evaluate the performance of the proposed procedure and to compare it with the NN-Sieve bootstrap. The distributions of the bootstrap variance estimators appear to be consistent, delivering good results both in terms of accuracy and bias, for either linear and nonlinear statistics (such as the mean and the median) and smooth functions of means (such as the variance and the covariance).


    In the present paper, we proved the existence of a ground state solution of the following nonlinear Choquard equation

    {Δu+V(x)u=(yxyZN|u(y)|p|xy|Nα)|u|p2u xZN,uH1(ZN), (1.1)

    on lattice graph ZN. This equation can be viewed as a discrete version of the following Choquard equation

    Δu+V(x)u=(Iα|u|p)|u|p2u xRN, (1.2)

    where α(0,N), p>1 and Iα:RNR is the Riesz potential defined at xRN{0} by

    Iα(x)=Aα|x|NαandAα=Γ(Nα2)Γ(α2)πN/22α,

    with Γ being the Euler gamma function.

    In the past few decades, many mathematicians have been devoted to studying the Eq (1.2), for example, see [1,2,3,4,5,6]. In particular, if N=3, V=1 and p=2, i.e., Δu+u=(I2|u|2)u, appeared in the literature at least as early as in 1954's work by Pekar on quantum theory of a Polaron at rest [7]. Later in the 1970s, Choquard utilized model (1.2) to describe an electron caught in its own hole, in an approximation to Hartree-Fock theory of one-component plasma [1]. Particularly, the equation is also knows as the Schrödinger-Newton equation, which was used to a model of self-gravitating matter [8]. Also, the article [9] used this system to study the pseudo-relativistic boson stars. In a pioneering work, Lieb [1] proved the existence and uniqueness of the ground state to the Eq (1.2) in R3 with V=1, α=2 and p=2. In the paper [3], Moroz and Van Schaftingen first obtained the sharp range of the parameter for the existence of solutions of the Eq (1.2) with N+αN<p<N+αN2. If V is the periodic function, since the nonlocal term is invariant under translation, the paper [10] got the existence results. Furthermore, Alves [11] proved the existence and convergence of nontrivial solutions of the nonlocal Choquard equation. There are tremendous results on this direction in [12,13,14,15,16,17,18] and the references therein.

    On the other hand, the analysis on the graph has become more and more popular, for example, see [19,20,21,22,23,24,25,26,27]. In a series of work of Grigor'yan et al. [19,20,21], they studied the Yamabe type equations, Kazdan-Warner equation and some other nonlinear equations on graph by using the variational methods. In [27], Zhang and Zhao investigated the existence of nontrivial solution of the equation Δu+(λa(x)+1)u=|u|p1u on the locally finite graphs by using Nehari methods (see [28]) and the asymptotic properties of the solution. Later, the paper [22] generalized the results of [27] to higher order. Furthermore, Hua and Xu [] obtained the existence results of nonlinear equation Δu+V(x)u=f on the lattice graph ZN. Recently, Huang et al. investigated extensively the Mean field equation and the relativistic Abelian Chern-Simons equations on the finite graphs by using the variational method in [23]. For other related results about the graph, we refer the reader to [29,30,31,32,33,34] and references therein.

    Inspired by the poineering works, in this paper we study the existence and asymptotical behavior of solution for the Choquard equation (1.2) on the lattice graph ZN. For clarity, let us introduce the basic setting on the lattice graph ZN. The graph ZN consists of the set of vertices

    V={x=(x1,,xN):xiZ,1iN},

    and the set of edges

    E={{x,y}:x,yZN,Ni=1|xiyi|=1}.

    For any two vertices x,yZN, the distance d(x,y) between them is defined by

    d(x,y):=inf{k:x=x1x2xk=y},

    where we write yx if and only if the edge {x,y}E. Assume ΩZN, we say Ω is bounded if d(x,y) is uniformly bounded for any x,yΩ. It is easy for us to see that a bounded domain of ZN can contain only finite vertices. We denote the boundary of Ω is

    Ω:={yΩ:xΩsuch that xyE}.

    C(ZN) denotes the set of real-valued functions on ZN. For any uC(ZN), its support set is defined as supp(u)={xZN,u(x)0}. Let Cc(ZN) denote the set of all functions of finite support. We can define the associated gradient for any function u,vC(ZN) by

    Γ(u,v)(x):=yx12(u(y)u(x))(v(y)v(x)).

    In particular, let Γ(u)=Γ(u,u) for simplicity. The length of the gradient of u is written by

    |u|(x):=Γ(u)(x)=(yx12(u(y)u(x))2)1/2.

    Let μ be the counting measure on ZN, i.e., for any subset AZN, μ(A):= #{x:xA}. For any function f on ZN, we write

    ZNfdμ:=xZNf(x),

    whenever it makes sense. p is a space endowed with the norm

    up(ZN):={(xZN|u(x)|p)1p1p<.supxZN|u(x)|p=.

    Assume uC(ZN), the Laplacican on ZN is defined as

    Δu=yx(u(y)u(x)).

    The inner product of the Hilbert space H1(ZN) is given by

    u,v:=ZN(Γ(u,v)+uv)dμ=ZN(uv+uv)dμ.

    Therefore, the corresponding norm reads

    uH1(ZN)=(ZN(|u|2+u2)dμ)12.

    For a bounded uniformly positive function V:ZNR, it is natural for us to consider the equivalent norm in H1(ZN) as

    u2:=ZN(|u|2+V(x)u2)dμ.

    Then we have the conclusions for the Eq (1.1).

    Theorem 1.1. Let NN, α(0,N) and p(N+αN,). Suppose that V(x):ZNR satisfies the conditions:

    (i) V is bounded uniformly positive, i.e. there exist constant C1,C2>0 satisfying C1<V(x)<C2 for any xZN.

    (ii) V is T-periodic, i.e. for the positive integer T, we have V(x+Tei)=V(x), xZN,1iN, where ei is the unit vector in the i-th coordinate.

    Then there exists a ground state solution of (1.1).

    Remark 1.2. The preceding theorem is a discrete version of the results in [3]. As in the paper [24], we use the Concentration-Compactness Principle (P. L. Lions [35,36]) to recover the compactness and prove the existence of ground state solution of (1.1). Interestingly, since the discreteness of the graph, the Sobolev embedding on the lattice graph is different from that in the continuous setting, which allows us to remove the upper critical exponents N+αNα in the continuous case.

    Next we turn to studying the convergence of the solution for the nonlinear Choquard equation. The results of Schrödinger type equation is already considered in the Euclidean space (see [11,37]). We may expect that the nonlocal Choquard equation on lattice graphs has some similar results. As the paper [22,27], we also consider the confining potential V=λa(x)+1, i.e.,

    Δu+(λa(x)+1)u=(yxyZN|u(y)|p|xy|Nα)|u|p2u. (1.3)

    To study the problem (1.3), we introduce the following subspace of H1(ZN):

    Eλ(ZN)={uH1(ZN):ZNλa(x)u2dμ<+}.

    It is easy to recognize that the scalar product of Eλ(ZN) is

    u,vEλ(ZN):=ZN(Γ(u,v)+(λa(x)+1)uv)dμ=ZN(uv+(λa(x)+1)uv)dμ.

    Then we have the following conclusions.

    Theorem 1.3. Let NN, α(0,N) and p[N+αN,). Suppose that a(x):ZNR satisfying

    (A1) a(x)0 and the potential well Ω={xZN:a(x)=0} is a non-empty, connected and bounded domain in ZN.

    (A2) There exists a point x0 satisfying a(x) when d(x,x0).

    Then (1.3) has a ground state solution uλ for any constant λ>1.

    In order to observe the asymptotical properties of uλ as λ, we first study the following Dirichlet problem.

    {Δu+u=(yxyΩ|u(y)|p|xy|Nα)|u|p2u in Ω.u=0on Ω. (1.4)

    We study the Eq (1.4) in the H10(Ω) with the norm:

    u2H10(Ω):=ΩΩ|u|2dμ+Ωu2dμ.

    Similarly, the Eq (1.4) also possess a ground state solution.

    Theorem 1.4. Let NN, α(0,N) and p(1,). Suppose Ω is a non-empty, connected and bounded domain in ZN. Then the Eq (1.4) has a ground state solution uH10(Ω).

    Finally, we show that the solutions uλ of (1.3) converge to a solution of (1.4) as λ when the domain in (1.4) is the set of satisfying a(x)=0. On the other words, we obtain the following conclusions.

    Theorem 1.5. Let NN, α(0,N) and p[2,). Assume that a(x) satisfies (A1) and (A2), then for any sequence λk, up to a subsequence, the corresponding ground state solutions uλk of (1.3) converge in H1(ZN) to a ground state solution of (1.4).

    The remaining parts of this paper are organized as follows. In Section 2, we give basic definitions and Lemmas on the lattice graph. In Section 3, we establish the discrete Brézis-Lieb Lemma for the nonlocal term and some important conclusions. Section 4 is devoted to proving Theorem 1.1. Then we complete the proof of Theorems 1.3 and 1.4 in Section 5. Finally, we prove Theorem 1.5 in Section 6.

    In this section we give some basic results on the lattice graph. Firstly, we present the formula of integration by parts on lattice graph, which is the basic conclusion when we apply variational methods. Here we omit the concrete proofs and one can refer to [22] for more details.

    Lemma 2.1. Suppose that uH1(ZN). Then for any vCc(ZN), we obtain

    ZNuvdμ=ZNΓ(u,v)dμ=ZNΔuvdμ. (2.1)

    Lemma 2.2. Suppose ΩZN is a bounded domain and uH10(Ω). Then for any vCc(Ω), we have

    ΩΩuvdμ=ΩΩΓ(u,v)dμ=ΩΔuvdμ. (2.2)

    Now we are ready to define the weak solution as follows.

    Definition 1. Assume uH1(ZN). A function u is called a weak solution of (1.1) if for any φH1(ZN),

    ZNuφdμ+ZNV(x)uφdμ=ZN(yxyZN|u(y)|p|xy|Nα)|u|p2uφdμ. (2.3)

    Definition 2. Assume uEλ(ZN). A function u is called a weak solution of (1.3) if for any φEλ(ZN),

    ZNuφdμ+ZN(λa(x)+1)uφdμ=ZN(yxyZN|u(y)|p|xy|Nα)|u|p2uφdμ. (2.4)

    Definition 3. Assume uH10(Ω). A function u is called a weak solution of (1.4) if for any φH10(Ω),

    ΩΩuφdμ+Ωuφdμ=Ω(yxyΩ|u(y)|p|xy|Nα)|u|p2uφdμ. (2.5)

    Notice that if u is a weak solution of (1.1), we infer from Lemma 2.1 that for any test function φH1(ZN),

    ZN(Δuφdμ+V(x)uφ)dμ=ZN(yxyZN|u(y)|p|xy|Nα)|u|p2uφdμ. (2.6)

    For any fixed x0ZN, choosing a test function φ:ZNR in (2.6) which is defined as

    φ(x)={1,x=x0,0,xx0,

    we obtain

    Δu(x0)+V(x0)u(x0)=(yx0yZN|u(y)|p|x0y|Nα)|u(x0)|p2u(x0),

    which implies that u is a point wise solution of (1.1). Thus, we have the following conclusion for the relationship between the weak solution and the point wise solution.

    Proposition 2.3. If u is a weak solution of (1.1), then u is a point wise solution. Similarly, if u is a weak solution of (1.3) or (1.4), then u is also a point wise solution of the corresponding equation.

    Finally, we state the following conclusions for the Sobolev embedding.

    Lemma 2.4. ([38]) H1(ZN) is continuously embedded into q(ZN) for any q[2,]. Namely, for any uH1(ZN), there exists a constant Cq depending only on q such that

    uq(ZN)CquH1(ZN). (2.7)

    Lemma 2.5. ([27,Lemma 2.6]) Assume that λ>1 and a(x) satisfies (A1) and (A2). Then Eλ(ZN) is continuously embedded into q(ZN) for any q[2,] and the embedding is independent of λ. Namely, there exists a constant Cq depending only on q such that for any uEλ(ZN), uq(ZN)CquEλ(ZN). Moreover, for any bounded sequence {uk}Eλ(ZN), there exists uEλ(ZN) such that, up to a subsequence,

    {uku in Eλ(ZN).uk(x)u(x) xZN.uku in q(ZN).

    Lemma 2.6. ([27,Lemma 2.7]) Assume that Ω is a bounded domain in ZN. Then H10(Ω) is continuously embedded into q(Ω) for any q[1,]. Namely, there exists a constant Cq depending only on q such that for any uH10(Ω), uq(Ω)CquH10(Ω). Moreover, for any bounded sequence {uk}H10(Ω), there exists uH10(Ω) such that, up to a subsequence,

    {uku in H10(Ω).uk(x)u(x) xΩ.uku in q(Ω).

    In this section, we give a proof of the discrete Brézis-Lieb Lemma(see [3,39,40] for the continuous case) for the nonlocal term on the lattice graph. First, let us recall the discrete Brézis-Lieb Lemma [38] for the local case.

    Lemma 3.1. ([38,Lemma 9]) Let ΩZN be a domain and {un}q(Ω) with 0<q<. If {un} is bounded in q(Ω) and unu pointwise on Ω as n, then

    limn(unqq(Ω)unuqq(Ω))=uqq(Ω). (3.1)

    From Lemma 3.1 and [38,Corollary 10], it is not hard for us to get the following corollary.

    Corollary 3.2. Assume V is a uniformly bounded positive function. If {un} is bounded in H1(ZN) and unu pointwise on ZN, then

    limn(ZN(|un|2+V(x)u2n)dμZN(|(unu)|2+V(x)(unu)2)dμ)=ZN(|u|2+V(x)u2)dμ. (3.2)

    Next, we prove a variant of the discrete Brézis-Lieb Lemma.

    Lemma 3.3. Let ΩZN be a domain, 1q<. If the sequence {un} is bounded in r(Ω) and unu pointwise on Ω as n, then for every q[1,r],

    limnΩ||un|q|unu|q|u|q|rqdμ=0. (3.3)

    Proof. Applying the Fatou's Lemma, we obtain

    ur(Ω)lim_nunr(Ω)<. (3.4)

    Fix ε>0 and for all a,bR, there exists Cε satisfying

    ||a+b|q|a|q|ε|a|q+Cε|b|q.

    Hence we obtain

    fεn:=(||un|q|unu|q|u|q|ε|unu|q)+(||un|q|unu|q|+|u|qε|unu|q)+(ε|unu|q+Cε|u|q+|u|qε|unu|q)+=(1+Cε)|u|q.

    Thus

    (fnε)rq(1+Cε)rq|u|r. (3.5)

    It follows from the Dominated Convergence Theorem that

    limnΩ(fnε)rqdμ=Ωlimn(fnε)rqdμ=0. (3.6)

    From the definition of fεn, we obtain

    ||un|q|unu|q|u|q|fεn+ε|unu|q.

    Moreover, one deduces from the basic inequality (a+b)pCp(ap+bp)(a,b,p>0) that

    ||un|q|unu|q|u|q|rq(fεn+ε|unu|q)rqCq,r((fεn)rq+εrq|unu|r). (3.7)

    Therefore, from (3.6) and (3.7), we get

    ¯limnΩ||un|q|unu|q|u|q|rqdμ¯limnCq,r(Ω(fnε)rqdμ+ZNεrq|unu|rdμ)Cq,rεrqsupnNunurr(Ω).

    Then let ε0,

    ¯limnΩ||un|q|unu|q|u|q|rqdμ=0.

    This finishes the proof.

    Next, we state the discrete Brézis-Lieb type Lemma.

    Lemma 3.4. Suppose ΩZN and 1p<. If the sequence {un} is bounded in p(Ω) and unu pointwise on Ω as n, then for every xZN, we have

    limn(yxyΩ|un(y)|p|xy|NαyxyΩ|un(y)u(y)|p|xy|Nα)=yxyΩ|u(y)|p|xy|Nα. (3.8)

    Proof. Since xy and x,yZN, we obtain |xy|1 and it follows that

    yxyΩ||un(y)|p|un(y)u(y)|p|u(y)|p||xy|NαyΩ||un(y)|p|un(y)u(y)|p|u(y)|p|.

    Thus the proof is complete as n from Lemma 3.3.

    Now we are in position to establish the discrete Brézis-Lieb Lemma for the nonlocal term of the functional. To this purpose we first present an important inequality on the lattice graph which is studied by many authors in the continuous setting.

    Lemma 3.5. ([41]) (Discrete Hardy-Littlewood-Sobolev Inequality) Let 0<α<N, 1<r,s< and 1r+1s+NαN2. Assume fr(ZN) and gs(ZN). Then there exists a positive constant Cr,s,α depending only on r,s,α such that

    x,yZNyxf(x)g(y)|xy|NαCr,s,αfr(ZN)gs(ZN). (3.9)

    The paper [38] also give the following equivalent form of (3.9).

    Lemma 3.6. Let 0<α<N, 1<r,t< and 1t+αN1r. Assume fr(ZN), then there exists a positive constant Cr,t,α depending only on r,t,α such that

    yZNyxf(y)|xy|Nαt(ZN)Cr,t,αfr(ZN). (3.10)

    The next lemma states the discrete Brézis-Lieb Lemma for the nonlocal term.

    Lemma 3.7. Let 1p< and the sequence {un} is bounded in 2NpN+α(ZN). Suppose unu pointwise on ZN as n, then

    limn(ZN(yxyZN|un(y)|p|xy|Nα)|un|pdμZN(yxyZN|un(y)u(y)|p|xy|Nα)|unu|pdμ)=ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ. (3.11)

    Proof. For every n, we can divide the left-hand side of (3.11) into two parts,

    ZN(yxyZN|un(y)|p|xy|Nα)|un|pdμZN(yxyZN|un(y)u(y)|p|xy|Nα)|unu|pdμ=ZN(yxyZN|un(y)|p|un(y)u(y)|p|xy|Nα)(|un|p|unu|p)dμ+2ZN(yxyZN|un(y)|p|un(y)u(y)|p|xy|Nα)|unu|pdμ=:J1+2J2, (3.12)

    where

    J1=ZN(yxyZN|un(y)|p|un(y)u(y)|p|xy|Nα)(|un|p|unu|p)dμ,J2=ZN(yxyZN|un(y)|p|un(y)u(y)|p|xy|Nα)|unu|pdμ.

    By Lemma 3.3, taking q=p, r=2NpN+α, one has

    limnZN||un|p|unu|p|u|p|2NN+αdμ=0. (3.13)

    We first give the estimate for the term J1. From the Hardy-Littlewood-Sobolev inequality (Eq 3.9), one deduces that

    |J1ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ|ZN(yxyZN||un(y)|p|un(y)u(y)|p|u(y)|p||xy|Nα)||un|p|unu|p|u|p|dμ+2ZN(yxyZN||un(y)|p|un(y)u(y)|p|u(y)|p||xy|Nα)|u|pdμ|un|p|unu|p|u|p22NN+α(ZN)+2|un|p|unu|p|u|p2NN+α(ZN)|u|p2NN+α(ZN).

    From (3.13) and u2NpN+α(ZN)lim infnun2NpN+α(ZN)<, it gives that

    limnJ1=ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ. (3.14)

    Now we give the estimate for J2. From the Banach-Alaoglu theorem, |unu|p0 weakly in 2NN+α(ZN) as n and (3.9), we deduce that

    J2=ZN(yxyZN|un(y)|p|un(y)u(y)|p|u(y)|p|xy|Nα)|unu|pdμ+ZN(yxyZN|u(y)|p|xy|Nα)|unu|pdμ|un|p|unu|p|u|p2NN+α(ZN)|unu|p2NN+α(ZN)+ZN(yxyZN|u(y)|p|xy|Nα)|unu|pdμ.

    We infer from (3.10) that

    yxyZN|u(y)|p|xy|Nα2NNα(ZN)CN,p,αup2NpN+α(ZN).

    Moreover, |unu|p0 in 2NN+α(ZN). Hence we know that

    limnZN(yxyZN|u(y)|p|xy|Nα)|unu|pdμ=0.

    Then one deduces from (3.13) that limnJ2=0. This together with (3.14), we get the results.

    In the present section we are devoted to the proof of Theorem 1.1. Obviously, for any function u:ZNR, the energy functional related to (1.1) is given by

    J(u)=12ZN(|u|2+V(x)u2)dμ12pZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ. (4.1)

    Notice that the functional J is well defined in H1(ZN). Indeed, assume that u2NPN+α(ZN), then by applying the Hardy-littlewood-Sobolev inequality (Eq 3.9) to the function f=|u|p2NN+α(ZN), we obtain

    ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμCN,p,α(ZN|u|2NpN+αdμ)N+αN. (4.2)

    It sufficient for us to confirm when the condition u2NPN+α(ZN) is satisfied. According to the Lemma 2.4, H1(ZN) is continuously embedded into 2NpN+α(ZN) if and only if pN+αN. Moreover, we infer from the inequality (Eq 3.9) that

    ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμCN,p,αu2pH1(ZN), (4.3)

    where the constant CN,p,α depends only on N,α and p. Based on the previous argument, the function J is meaningful.

    Next, we define the Nehari manifold related to (4.1) by

    N:={uH1(ZN){0}:J(u)u=0}={uH1(ZN){0}:ZN(|u|2+V(x)u2)dμ=ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ}.

    Let

    m=infuNJ(u).

    If there exists a function uN satisfying J(u)=m, then the function u is called a ground state solution. Obviously, u is a critical point of J.

    Next, we shall find the critical point of the functional (4.1).

    Proposition 4.1. Let NN, α(0,N) and p(1,). If uH1(ZN)2NpN+α(ZN){0} and V is a uniformly bounded positive function, there holds

    maxt>0J(tu)=(1212p)S(u)pp1,

    where

    S(u)=ZN(|u|2+V(x)u2)dμ(ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ)1p.

    Proof. For any t>0, we set

    s(t):=J(tu)=t22ZN(|u|2+V(x)u2)dμt2p2pZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ.

    By a direct computation,

    s(t)=tZN(|u|2+V(x)u2)dμt2p1ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ.

    When s(t)=0, we can obtain a unique tu such that s(tu)=0. Moreover, one has

    tu=(ZN(|u|2+V(x)u2)dμZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ)12p2.

    Since as 0<t<tu, s(t)>0 and as t>tu, s(t)<0, thus

    maxt>0J(tu)=J(tuu)=(1212p)(ZN(|u|2+V(x)u2)dμ(ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ)1p)pp1.

    This finishes the proof.

    Note that the ground state energy of J can be characterized as

    m=infuNJ(u)=infuH1(ZN){0}maxt>0J(tu)=infuH1(ZN){0}(1212p)S(u)pp1.

    In the next conclusion we show the infirmum of S(u) can be achieved by some nontrivial function.

    Proposition 4.2. Let NN, α(0,N) and p(N+αN,). Suppose that V is a uniformly bounded positive function, then there exists uH1(ZN) satisfying

    S(u)=inf{S(v):vH1(ZN){0}}.

    Combining with Propositions 4.1 and 4.2, we complete the proof of Theorem 1.1. Then we only need to focus on the proof Proposition 4.2 in the next. In the Euclidean space, we are familiar with the different kinds of the proof of Proposition 4.2. For example, a strategy consists in minimizing among radial functions and then prove with the symmetrization by rearrangement that a radial minimizer is a global minimizer. In our setting, the main difficulty for the analysis is that there is no proper counterpart for radial functions on ZN and moreover we do not have the compactness in this problem. To overcome the difficulty we borrow an idea of [42,Section 4](also see [24]) and use the constraint method to prove Proposition 4.2.

    Proof of Proposition 4.2. Set

    m=inf{S(u):uH1(ZN){0}},

    then we can get

    1m=sup{1S(u):uH1(ZN)andZN(|u|2+V(x)u2)dμ=1}.

    Let {un} be a minimizing sequence in H1(ZN) such that

    ZN(|un|2+V(x)u2n)dμ=1,

    and limn1S(un)=1m. By the discrete Hardy-Littlewood-Sobolev inequality (Eq 3.9), we obtain

    CN,p,α(ZN(yxyZN|un(y)|p|xy|Nα)|un|pdμ)1pun2NpN+α(ZN)unN+αNp2(ZN)un1N+αNp(ZN)unN+αNpH1(ZN)un1N+αNp(ZN). (4.4)

    Taking the limit from both sides, one can see

    CN,p,α(1m)1plim_nun1N+αNp(ZN). (4.5)

    Since p>N+αN, we obtain

    lim_nun(ZN)C>0. (4.6)

    Hence, there exists a subsequence {un} and a sequence {yn}ZN such that |un(yn)|C for each n. By translations, we define ˜un=:un(y+knT) with kn=(k1n,kNn) to ensure that (ynknT)Ω where Ω=[0,T)NZN is a bounded domain in ZN. Then for each ˜un,

    ˜un(Ω)|un(yn)|C>0.

    Moreover, by translation invariance, we infer from V(x) is T-periodic in x that

    1=ZN(|un|2+V(x)u2n)dμ=ZN(|˜un|2+V(x)˜un2)dμ

    and

    S(un)=S(˜un).

    Without loss of generality, we can get a minimizing sequence {un} satisfying un(Ω)C>0. Since Ω is bounded, there exists at least one point, say x0, such that un(x0)u(x0)C>0. Since the sequence {un} is bounded in H1(ZN), it follows that unu in H1(ZN) and unu0 pointwise on ZN. Then it follows from Corollary 3.2 and Lemma 3.7 that

    1m=limn(ZN(yxyZN|un(y)|p|xy|Nα)|un|pdμ)1pZN(|un|2+V(x)u2n)dμ=¯limn(ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ+ZN(yxyZN|un(y)u(y)|p|xy|Nα)|unu|pdμ)1pZN(|u|2+V(x)u2)dμ+ZN(|(unu)|2+V(x)(unu)2)dμ¯limn(ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ)1p+(ZN(yxyZN|un(y)u(y)|p|xy|Nα)|unu|pdμ)1pZN(|u|2+V(x)u2)dμ+ZN(|(unu)|2+V(x)(unu)2)dμ. (4.7)

    For every n, we have

    (ZN(yxyZN|un(y)u(y)|p|xy|Nα)|unu|pdμ)1p1mZN(|(unu)|2+V(x)(unu)2)dμ.

    Since u0, one has

    (ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ)1p1mZN(|(u)|2+V(x)(u)2)dμ,

    which yields

    (ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ.)1p=1mZN(|(u)|2+V(x)(u)2)dμ.

    By (4.7), one has

    limn(ZN(yxyZN|un(y)u(y)|p|xy|Nα)|unu|pdμ)1p=limn1mZN(|(unu)|2+V(x)(unu)2)dμ.

    By Fatou's Lemma, one gets

    ZN(|u|2+V(x)u2)dμlim infnZN(|un|2+V(x)u2n)dμ1.

    Then it is enough for us to prove that ZN(|un|2+V(x)u2n)dμ=1. Using a contradiction argument, suppose that

    0<ZN(|un|2+V(x)u2n)dμ=K<1.

    then by

    limnZN(|(unu)|2+V(x)(unu)2)dμ=limnZN(|un|2+V(x)u2n)dμZN(|u|2+V(x)u2)dμ=1K>0.

    However, (a+b)p>ap+bp if a,b>0. This yields a contradiction by (4.7).

    In this section we shall prove the existence result for (1.3) and (1.4) by using the standard variational methods. Obviously, the functional associated with the problem (1.3) is given by

    Jλ(u)=12ZN(|u|2+(λa(x)+1)u2)dμ12pZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ,

    where pN+αN. The corresponding Nehari manifold is defined as

    Nλ:={uEλ(ZN){0}:Jλ(u)u=0}={uEλ(ZN){0}:ZN(|u|2+(λa(x)+1)u2)dμ=ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ}.

    We define the least energy level mλ by

    mλ:=infuNλJλ(u).

    Then we first prove the Nehari manifold Nλ is nonempty.

    Lemma 5.1. The Nehari manifold Nλ is non-empty.

    Proof. For tR and fix a function uEλ(ZN){0} and, we define

    γ(t):=J(tu)tu=t2ZN(|u|2+(λa(x)+1)u2)dμt2pZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ.

    Since p>1 and u0, it is obvious that γ(t)>0 for small t>0 and that limtγ(t)=. Then there exists t0(0,) such that γ(t0)=0, which implies that t0uNλ.

    Next, we prove the least energy level mλ is positive.

    Lemma 5.2. We have mλ=infuNλJλ(u)>0.

    Proof. Since uNλ, then

    ZN(|u|2+(λa(x)+1)u2)dμ=ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμ.

    By Lemma 2.5 and (3.9), we obtain

    u2Eλ(ZN)=ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμCu2p2NpN+α(ZN)Cu2pEλ(ZN),

    where C is independent of λ. It follows from p>1 that

    uEλ(ZN)(1C)12(p1)>0. (5.1)

    This gives

    mλ=infuNλJλ(u)=(1212p)infuNλu2Eλ(ZN)(1212p)(1C)12(p1)>0.

    The next lemma states that the least energy mλ can be achieved.

    Lemma 5.3. The value mλ can be achieved by some uλNλ. Namely, there exists some uλNλ such that Jλ(uλ)=mλ.

    Proof. Take a minimizing sequence {uk}Nλ such that limkJλ(uk)=mλ. Since

    ok(1)+mλ=Jλ(uk)=p12puk2Eλ(ZN),

    we have that {uk} is bounded in Eλ(ZN), where limkok(1)=0. By Lemma 2.5, we can assume that there exists some uλEλ(ZN) such that

    {ukuλ in Eλ(ZN).uk(x)uλ(x) xZN.ukuλ in q(ZN).

    From the discrete Hardy-Littlewood-Sobolev inequality (Eq 3.9), we infer that

    ZN(yxyZN|uk(y)uλ(y)|p|xy|Nα)|ukuλ|pdμCukuλ2p2NpN+α(ZN).

    Therefore, one has

    limkZN(yxyZN|uk(y)uλ(y)|p|xy|Nα)|ukuλ|pdμ=0.

    Then from the Lemma 3.7, we get

    limkZN(yxyZN|uk(y)|p|xy|Nα)|uk|pdμ=ZN(yxyZN|uλ(y)|p|xy|Nα)|uλ|pdμ. (5.2)

    Since the Eλ norm is weakly lower semi-continuous, one has

    Jλ(uλ)=12uλ2Eλ(ZN)12pZN(yxyZN|uλ(y)|p|xy|Nα)|uλ|pdμlim infk(12uk2Eλ(ZN)12pZN(yxyZN|uk(y)|p|xy|Nα)|uk|pdμ)=lim infkJλ(uk)=mλ. (5.3)

    Next it suffices to show that uλNλ. We infer from (5.1) that

    0<cuk2Eλ(ZN)=ZN(yxyZN|uk(y)|p|xy|Nα)|uk|pdμ.

    This together with (5.2) which implies that

    0<cZN(yxyZN|uλ(y)|p|xy|Nα)|uλ|pdμ. (5.4)

    Therefore uλ0. Since ukNλ, we infer that

    uλ2Eλ(ZN)lim infkuk2Eλ(ZN)=lim infkZN(yxyZN|uk(y)|p|xy|Nα)|uk|pdμ=ZN(yxyZN|uλ(y)|p|xy|Nα)|uλ|pdμ.

    We use the contradiction argument to obtain our results. Assume that

    uλ2Eλ(ZN)<ZN(yxyZN|uλ(y)|p|xy|Nα)|uλ|pdμ.

    Similar as the proof of Lemma 5.1, there would exist a t(0,1) such that tuλNλ. This implies that

    0<mλJλ(tuλ)=(1212p)tuλ2Eλ(ZN)t2lim infk(1212p)uk2Eλ(ZN)=t2lim infkJλ(uk)=t2mλ<mλ.

    This contradicts the fact that mλ=infuNλJλ(u). Therefore we have uλNλ. Moreover, we infer from (5.3) that mλ is achieved by uλ.

    The following Lemma finishes the proof of Theorem 1.3.

    Lemma 5.4. uλNλ is a critical point for Jλ.

    Proof. It is enough for us to prove that for any ϕEλ(ZN), there holds

    Jλ(uλ)ϕ=0.

    Since uλ0, we can choose a constant ε>0 such that uλ+sϕ0 for all s(ε,ε). Furthermore, for every given s(ε,ε), we can find some t(s)(0,) satisfying t(s)(uλ+sϕ)Nλ. Indeed, t(s) can be taken as

    t(s)=(uλ+sϕ2Eλ(ZN)ZN(yxyZN|(uλ+sϕ)(y)|p|xy|Nα)|uλ+sϕ|pdμ)12p2.

    Obviously, we can get t(0)=1. Take a function γ(s):(ε,ε)R which is defined as

    γ(s):=Jλ(t(s)(uλ+sϕ)).

    For t(s)(uλ+sϕ)Nλ and Jλ(uλ)=infuNλJλ(u), γ(s) achieves its minimum at s=0. Together with uλNλ and Jλ(uλ)uλ=0, it follows that

    0=γ(0)=Jλ(t(0)uλ)[t(0)uλ+t(0)ϕ]=Jλ(uλ)t(0)uλ+Jλ(uλ)ϕ=Jλ(uλ)ϕ.

    Next we focus on the proof of Theorem 1.4. The functional associated with the Eq (1.4) is given by

    JΩ(u)=12ΩΩ|u|2dμ+Ωu2dμ12pΩ(yxyΩ|u(y)|p|xy|Nα)|u|pdμ. (5.5)

    We remark that uq(Ω)CuH10(Ω) for q[1,] by Lemma 2.6. Therefore, the functional JΩ(u) is well defined as pN+α2N. The corresponding Nehari manifold is defined as

    NΩ={uH10(Ω){0}:Jλ(u)u=0}={uH10(Ω){0}:ΩΩ|u|2dμ+Ωu2dμ=Ω(yxyΩ|u(y)|p|xy|Nα)|u|pdμ}. (5.6)

    Let mΩ be

    mΩ:=infuNΩJΩ(u).

    Since Ω contains only finite vertices, the proofs of the previous results can be easily applied to the Eq (1.4). Moreover, p>1 is enough for us to prove Theorem 1.4. Here we omit the details of the proofs.

    In the current section, we mainly focus on the asymptotical properties of the solution. That is, we show that the ground state solutions uλ of (1.3) converge to a ground state solution of (1.4) as λ. To accomplish this we first prove that any solution of (1.3) is bounded away from zero.

    Lemma 6.1. There exists a constant σ>0 which is independent of λ, such that for any critical point uEλ(ZN) of Jλ, we have uEλ(ZN)σ.

    Proof. From Lemma 2.5 and the inequality (Eq 3.9), one has

    0=J(u)u=ZN(|u|2+(λa(x)+1)u2)dμZN(yxyZN|u(y)|p|xy|Nα)|u|pdμu2Eλ(ZN)C2pu2pEλ(ZN),

    where C is independent of λ. Then we can choose σ=(1C)pp1 and Lemma 6.1 is proved.

    The next lemma studies the property of (PS)c sequence of Jλ.

    Lemma 6.2. For any (PS)c sequence {uk} of Jλ, there holds

    limkuk2Eλ(ZN)=2pp1c. (6.1)

    Furthermore, there would exist a constant C1>0 independent of λ, such that either cC1 or c=0.

    Proof. Since Jλ(uk)c and Jλ(uk)0 as k, we have

    c=limk(Jλ(uk)12pJλ(uk)uk)=limk(1212p)uk2Eλ(ZN)=p12plimkuk2Eλ(ZN),

    which gives (6.1). By Lemma 2.5 and (3.9), for any uEλ(ZN), we obtain

    Jλ(u)u=u2Eλ(ZN)ZN(yxyZN|u(y)|p|xy|Nα)|u|pdμu2Eλ(ZN)C2pu2pEλ(ZN). (6.2)

    Take ρ=(12C2p)12p2. If uEλ(ZN)ρ, we get

    Jλ(u)u12u2Eλ(ZN).

    Take C1=p12pρ2 and suppose c<C1. Since {uk} is a (PS)c sequence, it yields

    limkuk2Eλ(ZN)=2pp1c<2pp1C1=ρ2.

    Hence, for large k, we have

    12uk2Eλ(ZN)Jλ(uk)uk=ok(1)ukEλ(ZN),

    which implies that ukEλ(ZN)0 as k. It follows immediately that Jλ(uk)c=0 and the positive constant can be taken as C1=p12pρ2=(12C2p)1p1.

    Remark 6.3. If we take c=mλ, then there would exist a (PS)c sequence uk such that ukuλ when proving the existence of a ground state solutions uλ. Since the Eλk norm of uλk is weakly lower semi-continuous, then uλEλ(ZN) is bounded by 2pp1mλ.

    Next, we study the relationship between the ground states mλ and mΩ.

    Lemma 6.4. mλmΩ as λ.

    Proof. Notice that mλmΩ for every positive λ owing to NΩNλ. Take a sequence λk satisfying

    limkmλk=MmΩ, (6.3)

    where mλk is the ground state and uλkNλk is the corresponding ground state solution of (1.3). Then it follows M>0 from Lemma 6.2. According to Remark 6.3, we know that the Eλk norm of uλk is controlled by the constant 2pp1mΩ, which is independent of λk. Up to a subsequence, we can assume that uλk(x)u0(x) on ZN and for any q[2,+), uλku0 in q(ZN). Moreover, we get that u00 from Lemma 6.1.

    We first claim that u0|Ωc=0. If it is not true, we can find a point x0 satisfying u0(x0)0. Since uλkNλk, then

    Jλk(uλk)=p12puλk2Eλk(ZN)p12pλkZNa(x)u2λkdμp12pλka(x0)u2λk(x0).

    Since a(x0)>0,uλk(x0)u0(x0)0 and λk, we get

    limkJλk(uλk)=,

    which contradicts with the conclusion mλkmΩ. Since the norm H1(ZN) is weakly lower semi-continuous and (5.2), we get

    ΩΩ|u0|2dμ+Ωu20dμZN(|u0|2+u20)dμlim infkZN(|uλk|2+u2λk)dμlim infkZN(|uλk|2+(λka(x)+1)u2λk)dμ=lim infkZN(yxyZN|uλk(y)|p|xy|Nα)|uλk|pdμ=ZN(yxyZN|u0(y)|p|xy|Nα)|u0|pdμ.

    Noticing that u0|Ωc=0, we get

    ΩΩ|u0|2dμ+Ωu20dμΩ(yxyΩ|u0(y)|p|xy|Nα)|u0|pdμ. (6.4)

    Then there exists α(0,1] such that αu0NΩ, i.e.,

    ΩΩ|αu0|2dμ+Ω|αu0|2dμ=Ω(yxyΩ|αu0(y)|p|xy|Nα)|αu0|pdμ.

    This implies that

    JΩ(αu0)=p12p(ΩΩ|αu0|2dμ+Ω|αu0|2dμ)p12pZN(|αu0|2+|αu0|2)dμp12pZN(|u0|2+|u0|2)dμlim infkp12pZN(|uλk|2+(λka(x)+1)u2λk)dμ=lim infkJλk(uλk)=M.

    Consequently, MmΩ. Combining with (6.3), we get that

    limλmλ=mΩ.

    Next, we are devoted to proving Theorem 1.5.

    Proof of Theorem 1.5. We need to prove that for any sequence λk, the corresponding uλkNλk satisfying Jλk(uλk)=mλk converges in H1(ZN) to a ground state solution uΩ of (1.4) along a subsequence. According to Remark 6.3, the Eλk norm of uλk is uniformly bounded by the constant 2pp1mΩ, which is independent of λk. Consequently, we can assume that there would exist some u0 satisfying uλk(x)u0(x) in ZNand for any q[2,+), uλku0 in q(ZN). Moreover, we get that u00 from Lemma 6.1. As what we have done in Lemma 6.4, we can prove that u0|Ωc=0.

    First, we claim that

    λkZNa(x)u2λkdμ0,as k (6.5)

    and

    ZN|uλk|2dμZN|u0|2dμ. (6.6)

    If for some δ>0, there holds

    limkλkZNa(x)u2λkdμ=δ>0,

    we have

    ΩΩ(|u0|2+u20)dμ<ZN(|u0|2+u20)dμ+δlim infkZN(|uλk|2+(λka(x)+1)u2λk)dμ=lim infkZN(yxyZN|uλk(y)|p|xy|Nα)|uλk|pdμ=Ω(yxyΩ|u0(y)|p|xy|Nα)|u0|pdμ.

    Then there exists α(0,1) such that αu0NΩ. On the other hand, if

    lim infkZN|uλk|2dμ>ZN|u0|2dμ,

    we also have ΩΩ(|u0|2+u20)dμ<Ω(yxyΩ|u0(y)|p|xy|Nα)|u0|pdμ. Then in both cases, we can find α(0,1) such that αu0NΩ. Consequently, we have

    mΩJΩ(αu0)=p12p(ΩΩ|αu0|2dμ+Ω|αu0|2dμ)=p12pα2(ΩΩ|u0|2dμ+Ω|u0|2dμ)<p12pZN(|u0|2+|u0|2)dμlim infkp12pZN(|uλk|2+(λka(x)+1)u2λk)dμ=lim infkJλk(uλk)=mΩ,

    which arrives at a contradiction.

    To prove Theorem 1.5, we also need verify that u0 is a ground state solution of (1.4). The first step is to prove that u0 is a critical point of JΩ. Since Jλk(uλk)ϕ=0, for any ϕH10(Ω)H1(ZN), we have

    ZNuλkϕdμ+ZN(λka(x)+1)uλkϕdμ=ZN(yxyZN|uλk(y)|p|xy|Nα)|uλk|p2uλkϕdμ. (6.7)

    Since a(x)=0 in Ω and ϕ=0 in Ωc, there holds

    ΩΩuλkϕdμ+Ωuλkϕdμ=Ω(yxyZN|uλk(y)|p|xy|Nα)|uλk|p2uλkϕdμ. (6.8)

    Let k, the above equality becomes

    ΩΩu0ϕdμ+Ωu0ϕdμ=Ωlimk(yxyZN|uλk(y)|p|xy|Nα)|u0|p2u0ϕdμ. (6.9)

    Since uλku0 in p(ZN) with p2 and Lemma 3.4, we obtain

    ΩΩu0ϕdμ+Ωu0ϕdμ=Ω(yxyΩ|u0(y)|p|xy|Nα)|u0|p2u0ϕdμ, (6.10)

    which yields u0NΩ, and u0 is a solution of (1.4).

    Finally, we prove that u0 achieves the infimum of JΩ in NΩ.

    Jλk(uλk) =12ZN(|uλk|2+(λka(x)+1)u2λk)dμ12pZN(yxyZN|uλk(y)|p|xy|Nα)|uλk|pdμ=12ZN(|u0|2+u20)dμ12pZN(yxyZN|u0(y)|p|xy|Nα)|u0|pdμ+ok(1)=12ΩΩ|u0|2dμ+Ωu20dμ12pΩ(yxyΩ|u0(y)|p|xy|Nα)|u0|pdμ+ok(1)=JΩ(u0)+ok(1). (6.11)

    Since Jλk(uλk)=mλk, we get JΩ(u0)=mΩ by Lemma 6.4. Hence the function u0 is a ground state solution of (1.4).

    Finally, we have the following lemma for the convergence of the sequence {uλk}.

    Corollary 6.5. Furthermore, we have limkuλku0Eλk(ZN)=0.

    Proof. Indeed, since uλkNλk and u0|Ωc=0, we have

    uλku02Eλk(ZN)=ZN(|(uλku0)|2+(λka(x)+1)(uλku0)2)dμ=uλk2Eλk(ZN)+u02Eλk(ZN)2ZNuλku0dμ2ZNuλku0dμ=uλk2Eλk(ZN)+u02H10(Ω)2ΩΩuλku0dμ2Ωuλku0dμ=uλk2Eλk(ZN)+u02H10(Ω)2u02H10(Ω)+ok(1)=uλk2Eλk(ZN)u02H10(Ω)+ok(1)=ZN(yxyZN|uk(y)|p|xy|Nα)|uk|pdμΩ(yxyΩ|u0(y)|p|xy|Nα)|u0|pdμ+ok(1),

    which finishes the proof.

    This work was supported by NNSF of China (Grants 11971202), Outstanding Young foundation of Jiangsu Province No. BK20200042.

    The authors declare no conflict of interest.



    [1] J. P. Kreiss, Bootstrap procedures for AR(∞)-processes, in Bootstrapping and Related Techniques (eds. K.-H. Jockel, G. Rothe and W. Sendler), Springer, Heidelberg, (1992), 107-113.
    [2] P. Bühlmann, Sieve bootstrap for time series, Bernoulli, 3 (1997), 123-148.
    [3] P. J. Bickel and P. Bühlmann, A new mixing notion and functional central limit theorems for a sieve bootstrap in time series, Bernoulli, 5 (1999), 413-446.
    [4] A. M. Alonso, D. Peña and J. Romo, Forecasting time series with sieve bootstrap, J. Stat. Plann. Infer., 100 (2002), 1-11.
    [5] A. M. Alonso, D. Peña and J. Romo, On sieve bootstrap prediction intervals, Stat. Probabili. Lett., 65 (2003), 13-20.
    [6] A. Zagdanski, On the construction and properties of bootstrap-t prediction intervals for stationary time series, Probab. Math. Stati. PWN, 25 (2005), 133-154.
    [7] A. M. Alonso and A. E. Sipols, A time series bootstrap procedure for interpolation intervals, Comput. Stat. Data Anal., 52 (2008), 1792-1805.
    [8] P. Mukhopadhyay and V. A. Samaranayake, Prediction intervals for time series: a modified sieve bootstrap approach, Commun. Stat. Simul. Comput., 39 (2010), 517-538.
    [9] G. Ulloa, H. Allende-Cid and H. Allende Robust sieve bootstrap prediction intervals for contaminated time series, Int. J. Pattern Recognit. Artif. Intell., 28 (2014).
    [10] Y. Chang and J. Y. Park, A sieve bootstrap for the test of a unit root, J. Time Ser. Anal., 24 (2003), 379-400.
    [11] Z. Psaradakis, Blockwise bootstrap testing for stationarity, Stat. Probabili. Lett., 76 (2006), 562 -570.
    [12] D. S. Poskitt, Properties of the sieve bootstrap for fractionally integrated and non-invertible processes, J. Time Ser. Anal., 29 (2008), 224-250.
    [13] D. S. Poskitt, G. M. Martin and S. D. Grose, Bias reduction of long memory parameter estimators via the pre-filtered sieve bootstrap, arXiv preprint arXiv, 2014 (2014).
    [14] E. Paparoditis, Sieve bootstrap for functional time series, Ann. Stat., 46 (2018), 3510-3538.
    [15] M. Meyer, C. Jentsch and J. P. Kreiss Baxter's inequality and sieve bootstrap for random fields, Bernoulli, 23 (2017), 2988-3020.
    [16] J. P. Kreiss, E. Paparoditis and D. N. Politis, On the range of validity of the autoregressive sieve bootstrap, Ann. Stat., 39 (2011), 2103-2130.
    [17] M. Fragkeskou and E. Paparoditis, Extending the Range of Validity of the Autoregressive (Sieve) Bootstrap, J. Time Ser. Anal., 39 (2018), 356-379.
    [18] F. Giordano, M. La Rocca and C. Perna, Forecasting nonlinear time series with neural network sieve bootstrap, Comput. Stat. Data Anal., 51 (2007), 3871-3884.
    [19] F. Giordano, M. La Rocca and C. Perna, Properties of the neural network sieve bootstrap, J. Nonparametr. Stat., 23 (2011), 803-817.
    [20] G. B. Huang, Q. Y. Zhu and C. K. Siew, Extreme learning machine: theory and applications, Neurocomputing, 70 (2006), 489-501.
    [21] G. B. Huang, H. Zhou, X. Ding, et al., Extreme learning machine for regression and multiclass classification, IEEE Trans. Syst. Man Cybern. Part B, 42 (2012), 513-529.
    [22] W. Haerdle and A. Tsybakov, Local polynomial estimators of the volatility function in nonparametric autoregression, J. Econometrics, 81 (1997), 223-242.
    [23] J. Franke and M. Diagne, Estimating market risk with neural network, Stat. Decisions, 24 (2006), 233-253.
    [24] A. R. Barron, Universal approximation bounds for superpositions of a sigmoidal function, IEEE Trans. Inf. Theory, 39 (1993), 930-945.
    [25] K. Hornik, M. Stinchcombe and P. Auer, Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives, Neural Comput., 6 (1994), 1262-1275.
    [26] Y. Makovoz, Random approximates and neural networks, J. Approximation Theory, 85 (1994), 98-109.
    [27] X. Chen and H. White, Improved Rates and Asymptotic Normality for Nonparametric Neural Network Estimators, IEEE Trans. Inf. Theory, 45 (1999), 682-691.
    [28] X. Chen and X. Shen, Asymptotic Properties of Sieve Extremum Estimates for Weakly Dependent Data with Applications, Econometrica, 66 (1998), 299-315.
    [29] J. Zhang, Sieve Estimates via Neural Network for Strong Mixing Processes, Stat. Inference Stochastic Processes, 7 (2004), 115-135.
    [30] S. F. Crone and N. Kourentzes, Feature selection for time series prediction-A combined filter and wrapper approach for neural networks, Neurocomputing, 7 (2010), 1923-1936.
    [31] C. Wang, Y. Qi, M. Shao, et al., A fitting model for feature selection with fuzzy rough sets, IEEE Trans. Fuzzy Syst., 25 (2017), 741-753.
    [32] D. Yu and L. Deng, Efficient and effective algorithms for training single hidden- layer neural networks, Pattern Recognit. Lett., 33 (2012), 554-558.
    [33] K. Li, J. X. Peng and G. W. Irwin, A fast nonlinear model identification method, IEEE Trans. Autom. Control, 50 (2005), 1211-1216.
    [34] X. Yao, A review of evolutionary artificial neural networks, Int. J. Intell. Syst., 8 (1993), 539-567.
    [35] G. B. Huang, D. H. Wang and Y. Lan, Extreme learning machines: a survey, Int. J. Mach. Learn. Cybern., 2 (2011), 107-122.
    [36] S. Ding, H. Zhao, Y. Zhang, et al. Extreme learning machine: algorithm, theory and applications, Artif. Intell. Rev., 44 (2015), 103-115. doi: 10.1007/s10462-013-9405-z
    [37] G. Huang, G. B. Huang, S. Song, et al., Trends in extreme learning machines: A review, Neural Networks, 61 (2015), 32-48.
    [38] G. H. Huang, H. Zhou, X. Ding, et al., Extreme learning machine for regression and multiclass classification, IEEE Trans. Syst. Man Cybern. Part B, 42 (2012), 513-529.
    [39] G. B. Huang, L. Chen and C. K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks, 17 (2006), 879-892.
    [40] G. B. Huang and L. Chen, Convex incremental extreme learning machine, Neurocomputing, 70 (2007), 3056-3062.
    [41] G. B. Huang and L. Chen, Enhanced random search based incremental extreme learning machine, Neurocomputing, 71 (2008), 3460-3468.
    [42] J. Lin, J. Yin, Z. Cai, et al., A secure and practical mechanism of outsourcing extreme learning machine in cloud computing, IEEE Intell. Syst., 28 (1999), 35-38.
    [43] E. Cule and S. Moritz, ridge: Ridge Regression with Automatic Selection of the Penalty Parameter, R package version, (2019), https://CRAN.R-project.org/package=ridge.
    [44] Z. Cai, J. Fan and Q. Yao, Functional-coefficient regression models for nonlinear time series, J. Am. Stat. Assoc., 95 (2000), 941-956.
    [45] H. Kuswanto and P. Sibbertsen, Can we distinguish between common nonlinear time series models and long memory?, Discussion papers//School of Economics and Management of the Hanover Leibniz University., (2007).
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