均匀分布U〔θ-1/2,θ+1/2〕中参数θ的四种估计量

给出均匀分布U〔θ-1/2,θ+1/2〕中θ的四种估计量,并分别比较了四种估计量的优劣性.     

Razumikhin-type exponential stability criteria of neutral stochastic functional differential equations

The paper discusses both pth moment and almost sure exponential stability of solutions to neutral stochastic functional differential equations and neutral stochastic differential delay equations, by using the Razumikhin-type technique. The main goal is to find sufficient stability conditions that could be verified more easily then by using the usual method with Lyapunov functionals. The analysis is based on paper [X. Mao, Razumikhin-type theorems on exponential stability of neutral stochastic functional differential equations, SIAM J. Math. Anal. 28 (2) (1997) 389-401], referring to mean square and almost sure exponential stability.     

Stochastic functional differential equations with infinite delay

The stability and boundedness of the solution for stochastic functional differential equation with finite delay have been studied by several authors, but there is almost no work on the stability of the solutions for stochastic functional differential equations with infinite delay. The main aim of this paper is to close this gap. We establish criteria of pth moment ψγ(t)-bounded for neutral stochastic functional differential equations with infinite delay and exponentially stable criteria for stochastic functional differential equations with infinite delay, and we also illustrate the result with an example.     

关于二项分布、Poisson分布和几何分布的高阶矩的递推公式

给出了二项分布、Poisson分布和几何分布高阶矩的递推公式,避免了其它计算方法上的不便与误差.     

下临界维数Wiener sausage相交时间的中偏差

本文研究在下临界维数情形下Wiener sausage 的相交时间, 应用新近提出的高阶矩方法和经典的Feynman-Kac 半群方法, 得到该情况下Wiener sausage 相交时间的中偏差.     

随机Volterra-Levin方程的稳定性

In this paper, we investigate the exponential stability in pth moment as well as the almost surely exponential stability of solutions of stochastic Volterra-Levin equations (SVLEs in short) by the use of fixed point theorem for p ≥ 2. Our results extend and improve the corresponding results obtained in [3, 12], and the result in [12] is a special case of our results.     

用矩匹配方法计算非线性风险度

矩匹配方法是用来求解非线性风险度(Value at Risk，简称：VaR)的一种普遍性方法，它是先假定样本经验分布服从已知分布族，然后运用矩匹配估计方法估计相应的参数，得到资产回报样本的密度函数，再计算风险度VaR；本采用的Johnson分布族是矩匹配方法的直接应用，并且计算出来的结果与局部Monte．Carlo结果进行了比较。并通过实证分析认为这种方法是一种良好的计算非线性VaR方法。     

Precise rates in the law of the logarithm for the moment convergence in Hilbert spaces

Let （X, Xn; n ≥1） be a sequence of i.i.d, random variables taking values in a real separable Hilbert space （H, ｜｜·｜｜） with covariance operator ∑. Set Sn = X1 ＋ X2 ＋ ... ＋ Xn, n≥ 1. We prove that, for b 〉 -1,
lim ε→0 ε^2（b＋1） ∞ ∑n=1 （logn）^b/n^3/2 E{｜｜Sn｜｜-σε√nlogn}=σ^-2（b＋1）/（2b＋3）（b＋1） B｜｜Y｜^2b＋3
holds if EX=0,and E｜｜X｜｜^2（log｜｜x｜｜）^3bv（b＋4）〈∞ where Y is a Gaussian random variable taking value in a real separable Hilbert space with mean zero and covariance operator ∑, and σ^2 denotes the largest eigenvalue of ∑.     

Multiperiod Portfolio Optimization with Terminal Liability: Bounds for the Convex Case

This paper is concerned with an investor trading in multiple securities over many time periods in order to meet an outstanding liability at some future date. The investor is concerned with maximizing the expected profits from portfolio rebalancing under an initial wealth restriction to meet the future liabilities. We formulate the problem as a discrete-time stochastic optimization model and allow asset prices to have continuous probability distributions on compact domains. For the case of Markovian price uncertainty and convex terminal liability, we develop a simplicial approximation, under which bounds on the problem can be computed efficiently. Computations only require evaluating a dynamic programming recursion, which thus, allows its application to problems with a large number of trading periods. The bounds are tight in that they are exact in certain cases. Numerical results are given to demonstrate the computational efficiency of the procedure.     

Backward extensions of subnormal operators

The concept of backward extension for subnormal weighted shifts is generalized to arbitrary subnormal operators. Several differences and similarities in these contexts are explored, with emphasis on the structure of the underlying measures.