相依序列密度函数的经验似然推断

本文利用了强平稳$m-$相依序列的特殊性质,讨论了$m-$相依序列密度函数的经验似然推断, 给出了似然比统计量的极限分布,可构造参数的经验似然置信区间. 并且通过模拟计算来说明有限样本下应用经验似然方法的合理性.     

关于代数体函数的微分多项式

本文证明υ值代数函数的微分多项式是一λ值（1≤λ≤υ）代数体函数,即υ值代数体函数ω=ω（z)的微分多项式p(ω）可以被如下方程确定：[ελ（z）p^λ ελ-1(z)p^λ-1 … ε0(z)]^k=0这里ε0(z),ε1(z),…,ελ（z)为整函数且无公共零点,λ和k为正整数且λk=υ。     

Beurling-Ahlfors扩张的伸张函数与ID-同胚

本文研究实轴上同胚在上半平面的扩张.利用拟对称函数ρ对伸张函数D作了较精细的估计.同时借助于诱导的边界函数对D、ρ在边界附近的性质作了进一步刻划.作为应用,我们分别给出上半平面存在ID-同胚扩张和IID-同胚扩张的充分条件.     

Mapping properties of nonlinear integral operators and pre-Schwarzian derivatives

In this paper, nonlinear integral operators on normalized analytic functions in the unit disk are investigated in connection with the pre-Schwarzian derivative and the Hornich operation. In particular, several nontrivial relations between these operators and the class of strongly starlike functions will be deduced.     

Nonnormality and stochastic differential equations

A highly nonnormal Jacobian may give rise to large transients. This behaviour has been shown to have implications for (a) the relevance of linearising a nonlinear system and (b) the timestep restrictions required to keep a numerical method stable. Here, we show that nonnormality also manifests itself for stochastic differential equations. We give an example of a family of systems that is stable without noise, but can be made exponentially unstable in mean-square by a noise perturbation that shrinks to zero as the nonnormality increases. We then show via finite-time convergence theory that an Euler approximation shares the same property, giving a discrete analogue of the result. In memory of Germund Dahlquist (1925–2005).AMS subject classification (2000) 65C30, 34F05     

Lukasiewicz 命题逻辑系统一种新的理论的相容度

根据演绎定理和完备性定理,应用公式真度理论在Lukasiewicz命题模糊逻辑系统中讨论理论Γ的相容性,根据矛盾式■是Γ-结论的真度的大小,提出了一种新的极指标和相容度的概念.给出了理论Γ相容、不相容及其它相关结论的充分必要条件,并且获得了相容度与发散度之间联系的重要关系式.     

方向数据密度函数核估计的重对数律

本文讨论方向数据密度函数核估计的逐点收敛速度问题,在较为温和的条件下建立了该核估计的重对数律并给出了它的逐点最优收敛速度.     

关于Smarandache ceil函数的一个方程

研究了关于Sm arandache ceil函数的一个方程,并用初等方法得到了它的所有解.     

Estimation of a smooth quantile function under the proportional hazards model

The problem of estimating a smooth quantile function, Q(·), at a fixed point p, 0 < p < 1, is treated under a nonparametric smoothness condition on Q. The asymptotic relative deficiency of the sample quantile based on the maximum likelihood estimate of the survival function under the proportional hazards model with respect to kernel type estimators of the quantile is evaluated. The comparison is based on the mean square errors of the estimators. It is shown that the relative deficiency tends to infinity as the sample size, n, tends to infinity.     

不等式组与变分不等式的极大熵函数方法

利用极大熵函数方法将不等式组及变分不等式的求解问题转化为近似可微优化问题，给出了不等式组及变分不等式问题近似解的可微优化方法，得到了不等式组和变分不等式问题的解集合的示性函数．