无限长信号中值滤波列的收敛性

若x ={x(n) }_n∈Z是一实数列 ,对每一正整数 p ,x^(p) ={x^(p) (n) }_n∈Z表示x通过p次窗为 2k + 1的中值滤波后的序列 .证明了{x^{( 2p)} }p≥ 1和 {x^{( 2p -1)} }p≥ 1皆收敛 ,从而完全解决了无限长信号中值滤波的收敛性问题 .     

U-统计量对数律的精确渐近性

设{X_n, n ≥1}是独立同分布随机变量序列, U_n 是以对称函数(x, y) 为核函数的U -统计量. 记U_n =2/n(n-1)∑_{1≤i h(Xi, Xj), h1(x) =Eh(x, X2). 在一定条件下, 建立了∑n=2^∞(logn)^δ-1EUn²I {I U n |≥n ^1/2√lognε}及∑n=3^∞(loglognε)^δ-1/logn EUn² I {|U n|≥n^1/2√log lognε} 的精确收敛速度.}     

n阶泛函微分方程边值问题

本文研究下列n阶RFDE边值问题:x⁽ⁿ⁾(t)=f(t,x_t,x(t),x′(t),…,x^(n-1)(t)),　t∈［0,T ］,x(t)=φ(t),t∈［-r,0］;x′(0)=η,x″(0)=η₂,…,x^(n-2) (0)=η_n-2,x^(j)(T)=A,其中j∈I={0,1,2,…,n-1},得到了解的存在性和唯一性新的结果.     

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.     

强不变原理中的最佳速度

设{X_n}为i.i.d.r.v.s.,EX₁=0,EX₁~2=1,S_n=sum from i=1 to n(X_i),H(x)>0 (x≥0)为非降连续函数,对某γ>0和x₀>0,当x≥x₀时,x^-2-γH(x)非降,x^-1logH(x)非增,且x^-1logH(x)→0(x→∞),则有一标准Wiener过程{W(t),t≥0},使得 S_n-W(n)=O(invH(n))a.s.(n→∞)的充分必要条件是:对任何t>0有EH(t|X₁|)<∞.     

平稳序列中具有随机容量的多个随机中心秩顺序统计量的联合渐近分布

设｛X_n｝是平稳序列,是X₁⁽ⁿ⁾,…,X_n⁽ⁿ⁾的顺序统计量．以Mn表示随机容量,N_n（k）,k＝1,…,s表示随机中心秩,本文在强混合条件下得到了序列(?)的极限分布．     

B值随机元阵列加权和的Lr收敛性

设B是实可分的Banach空间,{Xni,Fni,un≤i≤vn,n≥1}是B值适应随机元阵列,{αni,un≤i≤vn,n≥1}是实数阵列,当0＜r＜1或1≤r≤p且B是p可光滑时,研究了∑vni=un aniXni的Lr收敛性,所得的结果推广并改进了许多已知的结果.     

三角域上Bernstein多项式的Lipschitz常数

设T是平面上以T₁,T₂,T₃为顶点的三角形,f(p)为定义在T上的函数,称Bⁿ(f,P):=(?)f(i/n,j/n,k/n)B_i,j,kⁿ(P),为f的n次Bernstein多项式,这儿B_i,j,kⁿ(P):(n!)/(i!j!k!)uⁱv^jω^k是Bernstein基函数,(u,v,w)是P关于T的重心坐标。 B.M.Brown等人对单变量的Bernstein多项式证明了如果f∈Lip_Aλ,0<λ≤1,则对所有的n,都有B^α(f,x)∈Lip_Aλ。本文的目的是对定义在三角域T:{(x,y):x≥0,y≥0,x+y≤1}上的Bernstein多项式证明了类似的结果: 设f(P)∈Lip_Aλ,0<λ≤1,则对所有的n,Bⁿ(f,P)∈Lip_(2^1/2λA)λ,并且,在一定意义上,常数2^1/2λA是最好的。 上述结果对于任意的锐角或直角三角形T,也是成立的。 最后还指出,当T可为钝角三角形时,则不存在同一常数C,使对所有的n和任意三角形T,有Bⁿ(f,P)∈Lip_cλ。     

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.}     

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)\ \ $\sum\limits_{{{\bf n}}\in {{\cal N}}^{d}} |n|^{r/2-2} P(\max\limits_{{\bf 1}\leq{\bf k}\leq{\bf n}}|S_{{\bf k}}|\geq \varepsilon \sqrt{|{\bf n}|\log |{\bf n}|})<\infty$, $\forall\varepsilon>M$.     

一类本原无向图的广义上指数的极图

设R（n,d)表示由全体恰含d个环点的n(n≥3）阶本原无向图所构成的集合,F(n,d,k)为R(n,d)中图的第k重上广义本原指数的量大值,1≤d≤n,2≤k≤n-1。本文给出了第k重上广义本原指数达到F(n,d,k)的极图的完全刻画。     

On Differences of Two kth Powers of Integers

The arithmetic function rk–(n) counts the number of ways to write a natural number n as the difference of two kth powers (k 3 fixed). The investigation of the asymptotic behavior of the Dirichlet summatory function of rk–(n) leads in a natural way to a certain error term Pk–(t). In this article we establish a mean-square upper bound and an -estimate for Pk–(t).     

Generating functions for kTH record times - a martingale approach

Some Martingale relations are obtained for the kth record times, allowing the derivation of the form of the generating functions for these random variables.Translated from Zapiski Nauchnykh Seminarov Leningradskogo Otdeleniya Matematicheskogo Instituta im. V. A. Steklova Akademii Nauk SSSR, Vol. 184, pp. 208–214, 1990.     

Record values with constraints

Record values are very popular in probability and mathematical statistics. There are many books and papers concerned with classical record values and record times, i.e., records in sequences of independent equally distributed random variables. In recent times, new types of record values (records in the F ^α-scheme, record values in sequences of unequally distributed random variables, records with confirmations, exceedance record values) have been proposed and examined. The present paper proposes another record scheme (so-called “records with constraint”). Various cases are studied in which these records may be useful. For these record values, we give their joint density functions and discover some of their properties. For particular cases of utmost importance, when the initial random variables are independent and have equal exponential distribution, we obtain fairly simple representations of records with constraints as sums of independent equally distributed random terms.     

记录值的联合分布

使用组合数学与概率论的方法研究了记录值示性符之和的分布,并得出第i个记录值X(i)的概率分布及记录值X(j)和X(i)的联合分布.     

On Statistical Inference Based on Record Values

We develop methodology for conducting inference based on record values and record times derived from a sequence of independent and identically distributed random variables. The advantage of using information about record times as well as record values is stressed. This point is a subtle one, since if the sampling distribution F is continuous then there is no information at all about F in the record times alone; the joint distribution of any number of them does not depend on F. However, the record times and record values jointly contain considerably more information about F than do the record values alone. Indeed, in the case of a distribution with regularly varying tails, the rate of convergence of the exponent of regular variation is two orders of magnitude faster if information about record times is included. Optimal estimators and convergence rates are derived under simple, specific models, and shown to be surprisingly robust against significant departures from those models. However, even under our special models the estimators have irregular properties, including an undefined information matrix. To some extent these difficulties may be alleviated by conditioning and by considering the relationship between maximum likelihood and maximum probability estimators.     

Generating Large Sequences of Normal Maxima via Record Values

In our recent paper [4], algorithms for generating normal record values were developed. The developed algorithms were faster and more efficient than currently existing algorithms for generating normal record values. Algorithm 2.2, presented in this paper, is the most efficient algorithm among the algorithms studied in [4]. It allows generating “long” sequences of record values (up to two billion record values). In the present paper, two algorithms for generating normal maxima are proposed, one of which is based on algorithm 2.2. It also allows the generation of maxima taken from “large” samples. An algorithm for generating record times in a general continuous case is also proposed in the present paper.     

Record values in sequences of sample ranges

The classical representation of record values in sequences of independent random variables with the standard exponential distribution E(1) as sums of exponentially distributed random summands plays an important role in the mathematical theory of records. A generalization of this representation is proposed. A new similar result that makes it possible to express the record values of sample ranges as sums of independent exponentially distributed random variables is obtained.     

A Markov Chain Model for Medical Record Analysis

This paper presents a Markov chain model for the analysis of a centralized medical record system in a large general hospital. The model is used to describe the dynamic behaviour of the age of records associated with patients' visits to a hospital and provides a method for the long-term planning of the medical record storage facilities.