最值理论中一个结论的证明

同济大学《高等数学》最值理论中有结论称“若一个函数在一个区间内可导且只有一个驻点,并且这个驻点是函数的极值点,那么此驻点也是该函数的最值点”,但并未给出证明。学生们对此频感好奇。受Fermat引理启发,利用反证法可获一个比此结论更为一般的定理。     

正则条件全竞赛空间与混合策略竞赛空间上的两个极值定理

在正则的全竞赛空间与混合策略竞赛空间推广并证明了两个新的最优决策存在定理．     

Estimation of a Bivariate Extreme Value Distribution

Several threshold methods have been proposed for the purpose of estimating a bivariate extreme value distribution from a sample of data whose distribution is only in its domain of attraction. An integrated view of these methods is presented which leads to the introduction of a new asymptotically consistent estimator of the dependence function characterizing the extreme dependence structure. Through Monte Carlo simulations, the new estimator is also shown to do as well as its competitors and to outperform them in cases of weak dependence. To the authors' knowledge, this is the first time that the small-sample behavior of nonparametric bivariate threshold methods has ever been investigated.     

Spin Top (Toward a Theorem on the Spin-Statistics Relation)

Theoretical and Mathematical Physics - We propose a model of a spin particle as an analogue of a quantum mechanical top. We show that for this model, we can prove the theorem on the relation...     

一个微分中值问题的推广

This paper presents a generalized form and its application to a problem, which was proposed by F.P. Callham.     

Testing Extreme Value Models

We consider the full statistical families of extreme value distributions and generalized Pareto distributions , where , and denote the shape, scale and location parameters, respectively. We consider the testing problems against and = 0 against 0, where and are treated as nuisance parameters. Showing local asymptotic normality (LAN), we derive asymptotic envelope power functions for test sequences and establish tests which attain these upper bounds. The finite sample size behavior is studied by simulations.     

Testing Extreme Value Conditions

A test statistic is developed that checks the validity of the extreme value conditions without specifiying the shape parameter of the limiting extreme value distribution.     

Iterative Estimation of the Extreme Value Index

Let {X_n, n ≥ 1} be a sequence of independent random variables with common continuous distribution function F having finite and unknown upper endpoint. A new iterative estimation procedure for the extreme value index γ is proposed and one implemented iterative estimator is investigated in detail, which is asymptotically as good as the uniform minimum varianced unbiased estimator in an ideal model. Moreover, the superiority of the iterative estimator over its non iterated counterpart in the non asymptotic case is shown in a simulation study.AMS 2000 Subject Classification: 62G32Supported by Swiss National Science foundation.     

Further Extension on a Theorem of the Value Distribution of f＇f^n

By using small function method, the following result is obtained. If f(z) is transcendental meromorphic and that Ψ(z) is non-zero meromorphic and that T(r, Ψ) =S(r, f), then (n 1)T(r,f)≤(-N)(r,1/f'fn-Ψ) 2(-N)(r,1/f) (-N)(r,f) S(r,f).     

有关微分中值定理的一个推广

给出了微分中值定理的一个高次幂形式的推广结果.     

广义Cauchy中值定理及其逆定理

将C auchy中值定理的条件进行适当减弱,得到了广义C auchy中值定理,从而推广了C auchy中值定理,并在凸函数的条件下,证明了其逆定理亦成立.     

一种概率分布的极值分位数的最优估计

在本文中, 我们构造了一种新的极值分位数估计, 给出了估计量的极限性质. 同时, 在渐近二阶矩最小的准则下, 利用子样本自助法给出了计算所构造的极值分位数估计时的样本点分割方法, 从理论上证明了这一极限结果, 说明了这种分割在渐近二阶矩最小的准则下是渐近最优分割, 同时提出了自适应的样本点分割的自助算法.     

积分中值定理的改进

本改进了(第一)积分中值定理的结论，证明了定理中的中间值ζ属于开区间(α，b)．在将定理条件适当加强后，(第一)积分中值定理可由微分中值定理证得，揭示了它们之间的内在联系。     

推广的微分中值定理

利用左右导数,研究弱化条件下的微分中值定理,给出微分学中值定理的一种推广形式.     

Estimation of Value at Risk by Extreme Value Methods

Value at Risk (VaR) is defined as a low quantile in the distribution of financial profits and losses. It is the most commonly used measure of market risk in the financial industry. The methods currently used for estimation of VaR have various short comings as they are not aimed specifically at modeling the tails of the distribution of profits and losses; extreme value methods may prove valuable towards improving the current estimation methods. In this paper we give an overview of the current state of the art in applying extreme value methods to financial data and the problems encountered when doing so. We compare the performance of methods currently used for estimation of VaR to the performance of various extreme value methods and outline advantages and drawbacks of the different methods.     

Smoothing the Moment Estimator of the Extreme Value Parameter

Let {X _n be a sequence of i.i.d. random variables whose common distribution F belongs to the domain of attraction of an extreme value law. A semi-parametric estimator of the extreme value parameter is the Dekkers, Einmahl and de Haan [8] moment estimator. Practical use of this estimator requires the problematic choice of a number k=k(n) of upper order statistics and there are few reliable guidelines for this choice. An averaging or smoothing technique is proposed for this estimator yielding a less volatile function of k which in practice aids estimation.