一类随机不动点定理及其特例

在随机拓扑度的基础上进一步研究了随机不动点问题,得到了若干新的随机不动点定理.作为特例,也给出了相应的确定性算子的边界不动点定理.     

关于三值R0命题逻辑系统的若干注记

在三值R0命题逻辑系统中证明了随机真度的MP、HS和交推理规则;提出了随机开放度,指出随机开放度与随机发散度是从两个不同的角度刻画了理论的相容程度,并得出对同一个理论而言,二者取值相等的结论.     

R0型命题逻辑系统的随机化

利用赋值集的随机化方法,在R0型n值命题逻辑系统和R0型模糊命题逻辑系统中提出了公式的随机真度和随机距离的概念,建立了随机度量空间.指出当取均匀概率时,随机真度就转化为计量逻辑学中的真度,从而建立了更一般的随机逻辑度量空间.     

随机泛函分析中的锐角原理及应用(英文)

在随机泛函分析中证明了著名的锐角原理和随机一一映射定理,并且得到了若干新的结果．     

随机非线性凝聚算子方程的随机解

利用随机拓扑度理论研究随机非线性凝聚算子,在一定条件下得到随机算子方程A（w,x）=μx的随机解和随机算子不动点的存在性,所得结论减弱了已知文献中相应定理的条件．     

关于随机算子若干问题的研究

研究了新的随机不动点指数的计算问题,利用随机不动点指数的理论推广了著名的Amann定理.提出了随机算子的随机渐进歧点的新概念,并且研究了随机k(ω)-集压缩算子的随机渐进歧点的一些问题,也得到了若干新的结果.     

关于任意非负随机序列随机和的一类随机偏差定理

本文引入任意随机变量序列随机极限对数似然比概念,作为任意相依随机序列联合分布与其边缘乘积分布“不相似”性的一种度量,利用构造新的密度函数方法来建立几乎处处收敛的上鞅,在适当的条件下,给出了任意受控随机序列的一类随机偏差定理.     

三值R_0命题逻辑系统中理论的随机发散度

在三值R_0命题逻辑系统中,给出了随机相似度和随机逻辑伪距离的基本性质.然后在随机逻辑度量空间中提出了理论的随机发散度,指出全体原子公式之集在随机逻辑度量空间中未必是全发散的,其是否全发散取决于给定的随机数序的分布.     

不连续随机算子随机不动点定理及其应用

该文利用半序理论和随机压缩映象原理,得到了一类不连续随机增算子随机不动点的唯一存在定理.作为应用,考虑了R~n中含间断项的一阶随机微分积分方程初值问题.     

关于随机非线性算子的若干定理

本文利用随机拓扑度研究了随机凝聚算子的随机不动点定理和随机方程A(w,x)=μx的随机解,以及随机全连续算子的固有值和固有函数,得到若干新结果．     

Strong Convergence of the Kernel Estimates of Nonparametric Regression Functions

Let (X, Y), (X_1, Y_1),\cdots, (X_n, Y_n) be i. i. d. random vectors taking values in R_d\times R with E(|Y|)<\infinity, To estimate the regression function m(x)=E(Y|X=x), we use the kernel estimate $m_n(x)=[\sum\limits_{i = 1}^n {K(\frac{{{X_i} - x}}{{{h_n}}}){Y_i}/} \sum\limits_{i = 1}^n {K(\frac{{{X_j} - x}}{{{h_n}}})} \]$ where K(x) is a kernel function and h_n a window width. In this paper, we establish the strong consistency of m_n(x) when E(|Y|^p)<\infinity for some p>l or E{exp(t|Y|^\lambda)}<\infinity for some \lambda>0 and t>0. It is remakable that other conditions imposed here are independent of the distribution of (X, Y).     

ON THE PROBLEM OF NECESSARY CONDITIONS ENSURING UNIFORM CONVERGENCE OF KERNEL DENSITY ESTIMATES

Let X_1,…,X,be a sequence of p-dimensional iid.random vectors with a commondistribution F(x).Denote the kernel estimate of the probability density of F(if it exists)by_n(x)=n~(-1)h~_n(-p)K((x-X_i)/h_n)Suppose that there exists a measurable function g(x)and h_n>0,h_n→0 such thatlim sup丨f_n(x)-g(x)丨=0 a.s.Does F(x)have a uniformly continuous density function f(x)and f(x)=g(x)?This paperdeals with the problem and gives a sufficient and necessary condition for generalp-dimensional case.     

和的乘积的重对数律

设{X,X_n,n≥1}是独立的或φ -混合的或 ρ -混合的正的平稳随机变量序列，或$\{X,X_n,n≥1}$是正的随机变量序列使得{X_n-EX,n≥1\} 是平稳遍历的鞅差序列，记S_n=\sum\limitsⁿ_{j=1}X_j, n≥1 . 该文在条件EX=μ> 0 及0 Var(X)<∞下，证明了部分和的乘积$\prod\limits^n_{j=1}S_j/n!\mu^n$在合适的正则化因子下的某种重对数律.     

CONVERGENCE ON RANDOMLY TRIMMED SUMS WITH A DEPENDENT SAMPLE

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Complete Convergence for Weighted Sums of Negatively Superadditive Dependent Random Variables

Let $\{X_n,n\geq1\}$ be a sequence of negatively superadditive dependent (NSD, in short) random variables and $\{a_{nk}, 1\leq k\leq n, n\geq1\}$ be an array of real numbers. Under some suitable conditions, we present some results on complete convergence for weighted sums $\sum_{k=1}^na_{nk}X_k$ of NSD random variables by using the Rosenthal type inequality. The results obtained in the paper generalize some corresponding ones for independent random variables and negatively associated random variables.     

Necessary Conditions of L_1-Convergenoe of Kernel Regression Estimators

Let (X_1,Y_1),\cdots,(X_n,Y_n) be iid. and R^d *R-valued samples of (X,Y). The kernel estimator of the regression function m(x)\triangleq E(Y|X=x) (if it exists), with kernel K, is denoted by $\[{m_n}(x) = \sum\limits_{i = 1}^n {{Y_i}K(\frac{{{X_i} - x}}{{{h_n}}})/\sum\limits_{j = 1}^n {K(\frac{{{X_j} - x}}{{{h_n}}})} } \]$ Many authors discussed the convergence of m_n(x) in various senses, under the conditions h_n\rightarrow 0 and nh_u^d\rightarrow \infinity asn\rightarrow \infinity. Are these conditions necessary? This paper gives an affirmative answer to this bprolemuithe case of L_1-conversence, when K satisfies (1.3) and E(|Y|log^+|Y|)<\infinity.     

Rate Optimality of Wavelet Series Approximations of Fractional Brownian Motion

Consider the fractional Brownian motion process $B_H(t), t\in [0,T]$, with parameter $H\in (0,1)$. Meyer, Sellan and Taqqu have developed several random wavelet representations for $B_H(t)$, of the form $\sum_{k=0}^\infty U_k(t)\epsilon_k$ where $\epsilon_k$ are Gaussian random variables and where the functions $U_k$ are not random. Based on the results of Kühn and Linde, we say that the approximation $\sum_{k=0}^n U_k(t)\epsilon_k$ of $B_H(t)$ is optimal if $$ \displaystyle \left( E \sup_{t\in [0,T]} \left| \sum_{k=n}^\infty U_k(t) \epsilon_k\right|^2 \right)^{1/2} =O \left( n^{-H} (1+\log n)^{1/2} \right), $$ as $n\rightarrow\infty$. We show that the random wavelet representations given in Meyer, Sellan and Taqqu are optimal.     

properties for Gaussian random series

Let be an arbitrary sequence of and let be a random series of the type

where is a sequence of independent Gaussian random variables and an orthonormal basis of (the finite measure space being arbitrary). By using the equivalence of Gaussian moments and an integrability theorem due to Fernique, we show that a necessary and sufficient condition for to belong to , for any almost surely is that . One of the main motivations behind this result is the construction of a nontrivial Gibbs measure invariant under the flow of the cubic defocusing nonlinear Schrödinger equation posed on the open unit disc of .

     

Commutator automorphisms of formal power series rings

For a big class of commutative rings , every continuous -automorphism of with the linear part the identity is in the commutator subgroup of . An explicit bound for the number of commutators involved and a -theoretic interpretation of this result are provided.

     

ON THE NECESSARY AND SUFFICIENT CONDITION OF THE EXISTENCE OF QUASI INVARIANT MEASURES

If E is a separable type-2 Banach space and Esub<0>sub is a linear subspace of E, then the following are equivalent: (a) There exists a probability measure \[\mu \] on E, Which is \[{E_{\text{0}}}\]-quasi-invariant. (b) There exists a sequence \[({X_n}) \subset E\] such that \[\sum {{e_n}(\omega ){X_n}} \] converges a.s., where \[{{e_n}(\omega )}\] are indepondend identically distributed symmetric stable random variables of index 2,i,e.\[E(\exp (it{\kern 1pt} {\kern 1pt} {e_n}(\omega ))) = exp( - \frac{{{t^2}}}{2})\]for all real t, and \[{E_{\text{0}}} \subset \{ x,x = \sum {{\lambda _n}{X_n}} ,\forall ({\lambda _n}) \in {l_2}\} \] In this note we prove that \[\sum {{\lambda _n}{X_n}} \] is convergent.