甩瓦累-布然平均逼近连续函数

<正> §1.总说§1.1 设 f(x)∈C_(2π),f(x)～a_0/2+sum form n=1 to ∞ a_ncosnx+b_nsin nx≡sum form n=0 to ∞ A_n(x)记 S_n(f,x)=sum form v=0 to n A_v(x).称σ_(n,p)(f,x)=1/p+1 sum form v=n-p to n S_v(f,x)为 f(x)的瓦累-布然平均.记△_u~kf(x)=sum form v=0 to k (-1)~v(?)f[x+(k-2v)u].称函数ω_k(f,t)=(?)|△~u_kf(x)|为 f(x)的 k 阶连续模.简记ω(f,t)=ω_1(f,t).假如 f(x)的共轭函数     

Precise asymptotics in the Baum-Katz and davis law of large numbers for positively associated sequences

§ 1　 Introduction and resultsL et { X,Xi;i≥ 1} be a sequence of i.i.d.random variables,and set Sn= ni=1 Xi,n≥1.Hsu and Robbins[1 ] introduced the conceptof complete convergence.They together withErdos[2 ] proved n≥ 1 P(|Sn|≥εn) <∞ ,ε>0 (1)if and only if EX=0 and EX2 <∞ .L ater,Spitzer[3] proved n≥ 11n P(|Sn|≥εn) <∞ ,ε>0if and only if EX =0 and E|X|<∞ .More generally,it was shown by Baum and Katz[4 ]that,for 0 0 (…     

方差分量组合的非负二次估计的可容许性

设有方差分量模型Y=X_β+U_(1ε1)+…+U_(NεN),其中XU_i已知,ε_1,…,ε_1相互独立。Eε_(if)=0,Eε_(if)~2=σ~2,Eε_(if)~3=0.Eε_(if)~4=3σ_i~4,这里(ε_(i1),…,ε_(in_i)εi。(β,σ~2)∈R~n×Ω为未知参数。Ω={(σ_1~2,…,σ_N~2):0≠sum from i=1 to n σ_i~2U_iU'_i≥0}。本文给出了Y'AY是sum from i=1 to n f_iσ_i~2在损失(Y'AY-sum from i=1 to N f_iσ_i~2)~2下在类{Y'BY:B≥0}中可容许估计的一个充分条件。同时也给出了Y'AY+l'Y+a是sum from i=1 to N f_iσ_i~2的可容许估计(在类{Y'BY+m'Y+b}中)的一个充要条件。研究了非负二次估计与局部最优估计之间的关系。     

关于Littlewood的一个问题

本文证明了: (1)如果{a_n}_n~N=1是非负不减序列,p>0,q>0,0≤r≤1,且p(q+r)≥q+p,则sum from n=1 to N(a_n~pA_n~q)(sum from m=n to N(a_n~(1+p/q)~r≤1·sum from n=1 to N(a_n~pA_n~q)~(1+p/q),其中A_n=sum from m=n to n (a_m).上述不等式在0≤r≤1时完全解决了H.Alzer~([4])在1996年提出的一个问题,且1是最佳常数; (2)如果{a_n}_n~N=1是非负序列,p,p≥1,r>0,r(p-1)≤2(q-1),令α=((p-1)(q+r)+p~2+1)/(p+1) β=(2p+2r+p-1)/(q+1),σ=(q+r-1)/(p+q+r)则sum from n=1 to N (a_n~p)sum from i=1 to n (a_i~qA_i~r)≤2~σsum from n=1 to N(a_n~αA_n~β)(0.2)(0.2)式改进了G.Be(?)et~([2,3])在1987年对Littlewood一个问题的结果,常数因子的3/2降为2~(3/2)=1.2598…     

局部时的Cauchy主值的精确完全收敛性

设{X,Xn;n≥1)为i.i.d.的随机变量序列,其均值为0且EX2=1.令S={Sn}n≥0为一维随机游动,其中S0=0,Sn=sum from k=1 to n Xk,对n≥1.定义G(n)为随机游动局部时的Cauchy主值.本文得到了,若存在某δ1>0,E|X|2r/(3p-4)+δ1<∞成立,那么对4/3P,有     

一个关于非负矩阵的不等式

证明:若(xij)是一个元素不全为零的m×n非负矩阵,则当0相似文献   

二次指派问题的一个新的限界方法

二次指派问题(QAP)的数学模型是:min{z(x)=sum from i=1 to n sum from =1 to n a_(ip)x_(ip)+sum from i=1 to n sum from p=1 to n sum from j=1 to n sum from q=1 to n c_(ipjq)x_(ip)x_(jq)|x∈},(1)这里∈(n~2维布尔集)是满足如下约束的集合:sum from i=1 to n x_(ip)=1,1≤p≤n,(2)sum from p=1 to n x_(ip)=1,1≤i≤n,(3)x_(ip)=0,1,1≤i,p≤n.(4)因为 x_(ip)~2=x_(ip)并且有约束(2)和(3),我们可以约定 c_(ipjq)=0,当 i=j 或 p=q.如果所有二次项的系数都可以写成     

随机场下重对数律的渐近性

设{X,Xk,k∈Zd+}是d维随机场独立同分布零均值的随机变量,β》-1/2,EX2=σ2,如果E[X2(log+|X|)α+d-1(log+log+|X|)β]《∞,则Sn=Σκ≤nXk,α》-1,β》-1/2,EX2=σ, ε(↓)σlim(2(α+d))[ε2-2(α+d)σ2(σ+d)σ2]β+1/2Σn(logㄧnㄧ)α(log logㄧnㄧ)β-ㄧnㄧP(ㄧSnㄧ≥εΓㄧnㄧlog log ㄧnㄧ)=2βσ-(d-1)!(2-(α+d))∏Γ(β+1/2), 其中Γ(·)为Gamma函数.由此回答了Gut和Spataru[4]在d=1时所提出的问题.     

关于误差方差估计渐近正态收敛速度的上,下界

考虑线性回归模型 Y_■=x_4~′β+e_■ i=1,2,…设误差序列■,i≥1满足条件:e_■ i≥1 i.i.d.,Ee_1=0,Ee_1~2=σ~2>0,∞>Var e_1~2=τ~2>0。记■_n~2=1/(n-r){sum from j=1 to n e■-sum from k=1 to r (sum from j=1 to n a_(akj)■_j)~2} δ(n)=τ~(-2)E(■_1~2-σ~2)~2I_((|■-σ~2|≥■τ)+τ~(-3)n~(1/2)|E(■_1~2-σ~2)~3I_((|■_1~2-σ~2|<(nτ)~(1/2))+τ~(-4)n~(-1)E■_1~2-σ~2)~4I_((|■-σ~2|0使得■|P(■_n~2-σ~2)/(Var■_n~2)~(1/2))≤x)-Φ(x)|≤C(δ(n)+n~(-1/2)) ■|P(■_n~2-σ~2)/(Var■_n~2)~(1/2))≤x)-Φ(x)|+n~(-1/2)≥C_1δ(n)。     

Asymptotic property for some series of probability

Let {X, X n , n≥1} be a sequence of i.i.d.random variables with zero mean, and set Sn = Σ k=1 n X k , EX2=σ 2>0, λ(ε) =Σ n=1 ∞ P (|Sn|≥ nε). In this paper, we discuss the rate of the approximation of σ2 by ε2 λ(ε) under suitable conditions, and improve the corresponding results of Klesov (1994).     

Approksimatsiya posredstvom eksponent na pryamoi i na polupryamoi

D Ranee avtorom byli postroeny sistemy vida $$e^{-i lambda_n t} \exp (-a|t|^\alpha), \quad \lambda_n \in \Lambda; \quad a>0, \ \alpha>1,\leqno(1)$$ polnye i minimal\cprime nye v prostranstvakh $L^p ({\bf R})$ $(L^p ({\bf R}_+))$ pri $p\ge 2$. V danno\u i\ stat\cprime e stroyat\cydot sya sistemy (1), obladayushchie takim svo\u istvom v prostranstvakh $L^p({\bf R})$ $(L^p ({\bf R}_+))$, $1\le p <2$ i $C_0 ({\bf R})$ $(C_0 ({\bf R}_+))$.     

Random coverings of the circle with i.i.d. centers

Consider the random intervals In(ω):=(ωn-ln/2,ωn+ln/2)(mod 1) with their centers ωn being i.i.d.but not necessary uniformly distributed on the circle T = R /Z and with their lengths decreasing to zero.Using the dimension theory in dynamical systems,we give conditions on which the circle is finitely or infinitely often covered by intervals In(ω)}n≥1.     

Asymptotic behaviour of codimensions of p. i. algebras satisfying Capelli identities

Let be a p. i. algebra with 1 in characteristic zero, satisfying a Capelli identity. Then the cocharacter sequence is asymptotic to a function of the form , where and .

     

A quenched invariance principle for non-elliptic random walk in i.i.d. balanced random environment

We consider a random walk on $\mathbb{Z }^d,\ d\ge 2$ , in an i.i.d. balanced random environment, that is a random walk for which the probability to jump from $x\in \mathbb{Z }^d$ to nearest neighbor $x+e$ is the same as to nearest neighbor $x-e$ . Assuming that the environment is genuinely $d$ -dimensional and balanced we show a quenched invariance principle: for $P$ almost every environment, the diffusively rescaled random walk converges to a Brownian motion with deterministic non-degenerate diffusion matrix. Within the i.i.d. setting, our result extend both Lawler’s uniformly elliptic result (Comm Math Phys, 87(1), pp 81–87, 1982/1983) and Guo and Zeitouni’s elliptic result (to appear in PTRF, 2010) to the general (non elliptic) case. Our proof is based on analytic methods and percolation arguments.     

Complete moment convergence for i.i.d. random variables

In the paper, the complete moment convergence is obtained for i.i.d. random variables such that all moments exist, but the moment generating function does not exist. The main results extend the related known works due to Gut and Stadtmüller.     

ON THE DIOPHANUNE EQUATION $\[\sum\limits_{i = 0}^N {\frac{{{x_i}}}{{{d_i}}}} \equiv 0\]$ (mod 1) AND ITS APPLICATIONS

The number $\[A({d_1}, \cdots ,{d_n})\]$ of solutions of the equation $$\[\sum\limits_{i = 0}^n {\frac{{{x_i}}}{{{d_i}}}} \equiv 0(\bmod 1),0 < {x_i} < {d_i}(i = 1,2, \cdots ,n)\]$$ where all the $\[{d_i}s\]$ are positive integers, is of significance in the estimation of the number $\[N({d_1}, \cdots {d_n})\]$ of solutiohs in a finite field $\[{F_q}\]$ of the equation $$\[\sum\limits_{i = 1}^n {{a_i}x_i^{{d_i}}} = 0,{x_i} \in {F_q}(i = 1,2, \cdots ,n)\]$$ where all the $\[a_i^''s\]$ belong to $\[F_q^*\]$. the multiplication group of $\[F_q^{[1,2]}\]$. In this paper, applying the inclusion-exclusion principle, a greneral formula to compute $\[A({d_1}, \cdots ,{d_n})\]$ is obtained. For some special cases more convenient formulas for $\[A({d_1}, \cdots ,{d_n})\]$ are also given, for example, if $\[{d_i}|{d_{i + 1}},i = 1, \cdots ,n - 1\]$, then $$\[A({d_1}, \cdots ,{d_n}) = ({d_{n - 1}} - 1) \cdots ({d_1} - 1) - ({d_{n - 2}} - 1) \cdots ({d_1} - 1) + \cdots + {( - 1)^n}({d_2} - 1)({d_1} - 1) + {( - 1)^n}({d_1} - 1).\]$$     

Large deviations for sums of i.i.d. random compact sets

We prove a large deviation principle for Minkowski sums of i.i.d. random compact sets in a Banach space, that is, the analog of Cramér theorem for random compact sets.

     

Tightness of products of i.i.d. random matrices

Summary In this paper we present a necessary and sufficient condition for tightness of products of i.i.d. finite dimensional random nonnegative matrices. We give an example illustrating the use of our theorem and treat completely the case of 2×2 matrices. We also describe stationary solutions of the linear equationy _n=X_ny_n–1, n>0, in (R ^d )⁺, whereX ₁,X ₂,... are i.i.d.d×d nonnegative matrices.     

The k-record processes are i.i.d.

Let X ₁, X ₂, ... be i.i.d. positive random variables, and let _n be the initial rank of X _n (that is, the rank of X _n among X ₁, ..., X _n). Those observations whose initial rank is k are collected into a point process N ^k on ⁺, called the k-record process. The fact that {itN^k; k=1, 2, ... are independent and identically distributed point processes is the main result of the paper. The proof, based on martingales, is very rapid. We also show that given N ¹, ..., N ^k, the lifetimes in rank k of all observations of initial rank at most k are independent geometric random variables.These results are generalised to continuous time, where the analogue of the i.i.d. sequence is a time-space Poisson process. Initially, we think of this Poisson process as having values in ⁺, but subsequently we extend to Poisson processes with values in more general Polish spaces (for example, Brownian excursion space) where ranking is performed using real-valued attributes.     

$l^{0}(\{X_{i}\})$型赋$F$-范空间的共轭空间的表示定理

作者在《数学学报》(2016, {\bf 59}(4))上的一篇文章中, 给出了几个$l^{0}$型赋$F$-范空间的共轭空间的表示定理. 对于赋范空间序列$\{X_{i}\}$,本文研究$l^{0}(\{X_{i}\})$型赋$F$- 范空间的共轭空间的表示问题,得到代数表示连等式$\left(l^{0}(\{X_{i}\})\right)^{*}\stackrel{A}{=}\left(c^{0}_{00}(\{X_{i}\})\right)^{*}\stackrel{A}{=}c_{00}(\{X^{*}_{i}\})$,$$\left(l^{0}(X)\right)^{*}\stackrel{\mathrm{A}}{=}\left(c^{0}(X)\right)^{*}\stackrel{\mathrm{A}}{=}\left(c_{0}^{0}(X)\right)^{*}\stackrel{\mathrm{A}}{=}\left(c^{0}_{00}(X)\right)^{*}\stackrel{\mathrm{A}}{=}c_{00}(X^{*}),$$以及序列弱星拓扑下的拓扑表示定理$\left(c^{0}_{00}(\{X^{*}_{i}\}),sw^{*}\right)=c^{0}_{00}(\{X^{*}_{i}\})$. 对于内积空间序列与通常拓扑下的数域空间序列,文章最后给出了基本表示定理的具体表现形式.