NA随机场对数律的收敛速度

设d是一个正整数, N ^d是d -维正整数格点.设{X_n , n∈N ^d} 是一同分布的负相伴随机场, 记S_n =∑_{k≤ n} X_k, S_n^(k)=S_n-X_k, 如果r >2, EX₁ = 0 和σ²= Var(X₁}, 则存在一个正数M:=100√(r-2)(1+σ²)使得下列条件等价 (I)E |X₁|^r (log|X₁|)^d-1-r/2 <∞; (II)∑_{n∈ N^d} |n|^r/2-2P(max_{1≤ k≤ n} |S_n^(k)|≥ (2^d+1 )ε√|n| log |n |) <∞,∨ε > M; (III)∑_{n∈N ^d} |n|^r/2-2P(max_{1≤ k≤n} |S_k |≥ε√| n} log| n |) <∞,∨ε > M. (III)\ \ $\sum\limits_{{{\bf n}}\in {{\cal N}}^{d}} |n|^{r/2-2} P(\max\limits_{{\bf 1}\leq{\bf k}\leq{\bf n}}|S_{{\bf k}}|\geq \varepsilon \sqrt{|{\bf n}|\log |{\bf n}|})<\infty$, $\forall\varepsilon>M$.     

部分和乘积的几乎处处中心极限定理

设X_n, n≥1是独立同分布正的随机变量序列, E(X₁)=u >0, Var(X₁)=σ², E|X₁|³<∞, 记S_n==∑^N_k=1X_k, 变异系数γ=σ/u.g是满足一定条件的无界可测函数, 证明了 lim_N→∞1/logN∑^N_n=11/n g((∏ⁿ_k=1S_k/n!uⁿ )^1/γ√n )=∫^∞₀g(x)dF(x),a.s., 其中 F(•) 是随机变量e^√2ξ 的分布函数, ξ 是服从标准正态分布的随机变量.     

一些与Dyck路有关的数的恒等式

本文通过Cauchy留数定理和算子方法导出了一些形如∑_i=0ⁿ (-1)^n-i(n i)U_{m+k+i, k+i} =f(n) 和∑_i=0²ⁿ(-1 )ⁱ(2n i) U_{m+k+i, k+i} = g(n)的差分恒等式,这里U_{n, κ}表示Dyck路在不同条件下的计数公式,f(n),g(n)与m(n)只和n有关的函数.     

关于亚纯代数体函数的奇异方向

设T(r, w)满足:lim_{r →∞}lg T(r, w)/lg r =0,lim_r→∞lg T(r, w)/lg lg r =+ ∞, 则一定存在一条方向arg z=θ₀ ,使对任意给定N>0,任意复数 a (至多有2 v个例外值), 有∑_i1/(lg|z_i(a;?(θ₀,δ))|)^N=∞.设T(r, w)满足:lim_r→∞T(r, w)/lg^Kr =+∞,lim_r→+∞lg T(r, w) /lg lg r =M, 则一定存在一条方向argz=θ₀ ,对任意复数a (至多有2 v个例外值),有∑_i1/lg|z_i(a;?(θ₀,δ))|)^σ=∞(σ = M-2或σ = M-2-ε.即使在亚纯函数,这些奇异方向也未见研究.     

类对称函数的Schur凸性和不等式

对x = (x₁, x₂,···, x_n) ∈ (0,1)ⁿ 和 r ∈ {1, 2,···, n} 定义对称函数 F_n(x, r) = F_n(x₁, x₂,···, x_n; r) =∏_{1≤i1∑j=1^r(1+xi3/1- xi3)^1/r, 其中i1, i2, ···, ir 是整数. 该文证明了Fn(x, r) 是(0,1)ⁿ 上的Schur凸、Schur乘性凸和Schur调和凸函数. 作为应用,利用控制理论建立了若干不等式.}     

关于k阶记录时间与k阶记录值的联合分布

设独立同分布随机变量序列{x_nj n≥1}的分布函数F(x)=p(x₁(k)(n);n≥1},{X^(k)(n);n≥1} 分别为{x_nj n≥1}的K阶记录时间序列和k阶记录值序列.本文我们用直接方法求出了{U^(k)(i),X^(k)(i);1≤i≤n}的联合分布，从而证明了k阶记录时间序列及k阶记录值序列的马氏性，并导出了它们之间的一     

B-值随机变量序列叠对数律的收敛速度

设{X,X_n;n≥1}是在可分Banach空间(B,‖·‖)中取值的独立同分布随机变量序列,并且EX=0,Ef²(X)<+∞,f∈B^*,记S_n=X₁+…+X_n,n≥1.本文的目的是在适当的充要条件下研究和的收敛速度.作为本文结果的应用,分别给出了X满足有界叠对数律和紧叠对数律各一个新的充要条件;同时,本文改进了文献[3]和[4]在实空间情形所建立的一些结果.     

Gauss-Markov条件下最小二乘估计的强相合性

设Y_i=x＇_iβ+e_i,1≤i≤n为线性模型,β_n=(β_n1,…,β_np)＇为β=(β₁,…,β_p)＇的最小二乘估计,以u_n记(sum from i=1 to n(x_ix＇_i))的(1,1)元,v_n=u_n^-1.证明了在Ee_i=O且{e_i}满足Gauss-Markov条件时,v_i→∞及sum from i=2 to ∞(v_i^-2(v_i-v_i-1)log~2i<∞)为β_n1强相合的充分条件,且对任何ε_n→0,v_i→∞及sum from i=2 to ∞(ε_iv_i^-2(v_i-v_i-1)log²i<∞)已不再充分.提出了β_n1强相合的一个充要条件,它把β_n1强相合归结为正交随机变量级数的收敛问题.     

二阶常微分方程无穷多点边值问题的正解

该文运用锥上的不动点定理研究非线性二阶常微分方程无穷多点边值问题 u'+a (t ) f (u)=0, t∈(0, 1), u(0)=0, u(1)=∑^∞_{i =1}α _i u ( ξ _i ) 正解的存在性. 其中ξ _i∈ (0,1),α _i∈ [0,∞), 且满足∑^∞_i=1α_iξ _i <1.α∈C([0,1], [0,)),f∈C ([0,∞), [0,∞)).     

酉群上Fourier级数的Cesaro求和

设un为n阶酉群。u∈L1(Un)的Fourier级数的第二型Cesáro平均为σNα(u,U)=KN*αu(U),其中 KNα(U)=sum from (N≥li>…>ln≥-N)(Al1α…A1uN(f)Xf(U)),U∈Un为相应的核函数。本文给出“Lebesgue常数”‖KNα‖(L1(Un))的精确估计,并由此建立了酉群上函数的Fourier级数按第二型Cesáro求和收敛于自身的条件。     

Strong law of large numbers for the middle part of a sample

Let {X_n} _n=1 be a sequence of independent, symmetric random variables and let {X_in} _i=1 ⁿ be the absolute order statistics. The rate of growth of and X_2,n is investigated for n.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova AN SSSR, Vol. 166, pp. 25–31, 1988.     

Limit theorems for trimmed sums

This paper studies the heavily trimmed sums (*) _{[ns] + 1} ^[nt] X _j ⁽ⁿ⁾ , where {X _j ⁽ⁿ⁾ } _{j = 1} ⁿ are the order statistics from independent random variables {X ₁,...,X _n } having a common distributionF. The main theorem gives the limiting process of (*) as a process oft. More smoothly trimmed sums like _{j = 1} ^[nt] J(j/n)X _j ⁽ⁿ⁾ are also discussed.     

Law of the iterated logarithm for perturbed empirical distribution functions evaluated at a random point for nonstationary random variables

We consider perturbed empirical distribution functions , where {Ginn, n1} is a sequence of continuous distribution functions converging weakly to the distribution function of unit mass at 0, and {X _{i, i}1} is a non-stationary sequence of absolutely regular random variables. We derive the almost sure representation and the law of the iterated logarithm for the statistic whereU _n is aU-statistic based onX ₁,...,X _n . The results obtained extend or generalize the results of Nadaraya,⁽⁷⁾ Winter,⁽¹⁶⁾ Puri and Ralescu,^(9,10) Oodaira and Yoshihara,⁽⁸⁾ and Yoshihara,⁽¹⁹⁾ among others.Research supported by the Office of Naval Research Contract N00014-91-J-1020.     

The Law of Iterated Logarithm of Rescaled Range Statistics for AR（1） Model

Let {Xn,n ≥ 0} be an AR（1） process. Let Q（n） be the rescaled range statistic, or the R/S statistic for {Xn} which is given by （max1≤k≤n（∑j=1^k（Xj - ^-Xn）） - min 1≤k≤n（∑j=1^k（ Xj - ^Xn ））） /（n ^-1∑j=1^n（Xj -^-Xn）^2）^1/2 where ^-Xn = n^-1 ∑j=1^nXj. In this paper we show a law of iterated logarithm for rescaled range statistics Q（n） for AR（1） model.     

Asymptotic behavior of self-normalized trimmed sums: Nonnormal limits II

Let {X _j} be independent, identically distributed random variables which are symmetric about the origin and have a continuous nondegenerate distributionF. Let {X _n(1),...,X _n(n)} denote the arrangement of {X ₁,...,X _n} in decreasing order of magnitude, so that with probability one, |X _n(1)|>|X _n(2)|>...> |X _n(n)|. For initegersr _n such thatr _n/n0, define the self-normalized trimmed sumT _n= _i=rn ⁿ X _n(i)/{ _i=rn ⁿ X _n ² (i)}^1/2. Hahn and Weiner⁽⁶⁾ showed that under a probabilistically meaningful analytic condition generalizing the asymptotic normality criterion forT _n, various nonnormal limit laws forT _n arise which are represented by means of infinite random series. The analytic condition is now extended and the previous approach is refined to obtain limits which are mixtures of a normal, a Rademacher, and a law represented by a more general random series. Each such limit law actually arises as can be seen from the construction of a single distribution whose correspondingL(T _n ) generates all of the law along different subsequences, at least if {r _n} grows sufficiency fast. Another example clarifies the limitations of the basic approach.     

A proof of Freud's conjecture for exponential weights

LetW (x) be a function nonnegative inR, positive on a set of positive measure, and such that all power moments ofW ²(x) are finite. Let {p _n (W ²;x)} ₀ denote the sequence of orthonormal polynomials with respect to the weightW ²(x), and let {A _n } ₁ and {B _n } ₁ denote the coefficients in the recurrence relation

A Note on the Asymptotic Normality of Sample Autocorrelations for a Linear Stationary Sequence

We consider a stationary time series {X_t} given byX_t=∑^∞_k=−∞ ψ_kZ_t−k, where {Z_t} is a strictly stationary martingale difference white noise. Under assumptions that the spectral densityf(λ) of {X_t} is squared integrable andm^τ ∑_|k|?m ψ²_k→0 for someτ>1/2, the asymptotic normality of the sample autocorrelations is shown. For a stationary long memoryARIMA(p, d, q) sequence, the conditionm^τ ∑_|k|?m ψ²_k→0 for someτ>1/2 is equivalent to the squared integrability off(λ). This result extends Theorem 4.2 of Cavazos-Cadena [5], which were derived under the conditionm ∑_|k|?m ψ²_k→0.     

亚纯函数在角域内的波莱耳方向

Suppose that f(z) is a meromorphic function of order λ(0<λ≤∞) and of lower order μ(0≤μ<∞) in the plane. Let ρ(μ≤ρ≤λ) be a finite positive number. B: arg z=θ₀(0≤θ₀ <2π) is called a Borel direction of order ρ of f(z), if for any complex number a, the equality holds, except at most for some a belonging to a set of linear measure zero. For the exceptional values a, we have ρ(θ₀, a)>ρ, except two possible values. With the above hypotheses on f(z), λ, μ and ρ, We have the following lemmas. Lemma 1. There exists a sequence of positive numbers (r_n) such that(?)=∞ and that Lemma 2. If f(z) has a deficient value a₀ with deficiency δ(a₀, f), then we have where (r_n) is the sequence defined in the Lemma 1 and when a_0=∞, we have to replace(?)by (?) in the left hand side of (*). Lemma 3. Suppose that B_1 : arg z =θ₁ and B₂ : arg z=θ₂ (0≤θ₁<θ₂<2π+θ₁) are two half straight lines from the origin and there are no Borel directions of order≥ρ(ρ>1/2) of f(z) in θ₁₀, the inequality holds as n is sufficiently large, where K₁ is a positive number not depending on n andεand when a₀=∞, it is necessary to replace we have θ₂-θ₁≤π/ρ. Theorem 1. Suppose that f(z) is a meromorphic function of order λ (1/2<λ≤+∞) and of lower order μ(0≤μ<+∞) in the plane. Let p be a number such that μ≤ρ≤λ and that 1/2<ρ<+∞If f~((k))(z) has p(1≤P<+∞) deficient values a_i (i=1,2,…,p) with deficiencies δ(a_i,f^(k)), then f(z) has a Borel direction of order ≥ρ in any angular domain, the magnitude of which is larger than It is convenient to consider Julia directions as Borel directions of order zero.Under this assumption, We have the following. Theorem 2. Suppose that f(z) is a meromorphic function of order λ and of finite lower order μ in the plane and that ρ(μ≤ρ≤λ) is a finite number. If p denotes the number of deficient values of f(z) and q denotes the number of Borel directions of order ≥p of f(z), then we have p≤q.     

Moderate deviations and Erdös-Rényi-Shepp laws for weighted sums

Let X₀,X₁,… be i.i.d. random variables with E(X₀)=0, E(X²₀)=1 and E(exp{tX₀})<∞ for any |t|<t₀. We prove that the weighted sums V(n)=∑_j=0^∞a_j(n)X_j, n?1 obey a moderately large deviation principle if the weights satisfy certain regularity conditions. Then we prove a new version of the Erdös-Rényi-Shepp laws for the weighted sums.     

Lacunary subsets of orthonormal sets

В РАБОтЕ РАссМАтРИВА УтсьS _Р-пОДсИстЕМы О. Н.с. В ЧАстНОстИ, ДОкАжыВА Етсь слЕДУУЩАь тЕОРЕ МА, кОтОРАь НЕУсИльЕМА. тЕОРЕМА.пУсть Р>2 —ЧЕ тНОЕ ЧИслО, δ — пРОИжВО льНОЕ ЧИслО, 0<δ≦p?2,Φ= {Φ _n(x)} _n=1 ^N —O.H.C.,x?[0,1],пРИЧЕМ ∥ Φ _n∥_p≦M, n=1,2,...,N, гДЕР=Р+δ, 0М<∞. тОгДА Иж сИстЕМы Ф МОж НО ВыБРАть пОДсИстЕМ У \(\Phi ' = \left\{ {\varphi _{n_k } } \right\}_{k = 1}^{N'} ,N' \geqq N^{\alpha (\delta )} ,\alpha (\delta ) = \frac{{2\delta }}{{p(p - 2 + \delta )}}\) , тАкУУ, ЧтО Дль лУБОгО п ОлИНОМА \(P(x) = \sum\limits_{k = 1}^{N'} {a_k \varphi _{n_k } (x)} \) ИМЕЕ т МЕстО ОцЕНкА $$(\mathop \sum \limits_{k = 1}^{{\rm N}'} a_k^2 )^{1/2} \leqq \left\| P \right\|_p \leqq c_{p,M,\delta } (\mathop \sum \limits_{k = 1}^{{\rm N}'} a_k^2 )^{1/2} $$ (c _p, m, δ — пОстОьННАь, жАВИ сьЩАь тОлькО Отp, M, δ, НО НЕ От N ИлИ кОЁФФИцИЕНтОВ пО лИ-НОМА). пРИВОДьтсь И ДРУгИЕ РЕжУльтАты А НАлОгИЧНОгО хАРАктЕ РА.