一类边值问题的有限差分牛顿型方法

本文利用微分方程的非线性差分格式的特殊结构，提出了一种新的牛顿型方法求解非线性差分方程，若新方法每步不队加计算非线性方程组的函数值，那么新自满收敛速度可在室Ｒ－１＋√４／２阶；若新方法每步附加计算一个非线性方程组的向量函数值，那么新算法收敛速度可达到Ｑ－平方阶。     

混合情形下的信息系统有效性——一个比较标准与数值算例

本文关注于委托代理问题中信息系统的选择问题,我们证明了在逆向选择和道德风险相对独立的混合情形下,关于代理人类型信息的累积分布函数满足一阶随机占优(FSD)的选择标准,关于代理人行为信息的累积分布函数满足二阶随机占优(SSD)的选择标准。最后,通过一个数值算例对上述选择标准的含义进行直观解释。     

一阶条件方法的有效性及其求解分析

委托代理问题的一阶条件方法,是将代理人激励相容约束进行松弛处理,利用代理人效用函数的稳定点来代替最大化效用约束．这对于问题带来了数学处理上的方便, 但是一般情况下,这种方法是无效的．本文对于求解委托代理问题的一阶条件方法的背景,一阶条件方法的有效性,实用性进行了分析,并给出了其中一类问题的解决方法．     

相关免疫函数阶的判别方法

在密码学中,为了抵抗相关攻击,要求选用的布尔函数具有相关免疫性,高阶的相关免疫函数都是一阶的,一阶的却不一定是高阶的,本文给出了二种判断一阶相关免疫函数是否为二阶或更高阶的新方法.     

集合函数多目标规划的一阶最优性条件

在文（１）－（４）的基础上，本文通过引入集团函数的伪凸，严格伪凸，拟凸，严格拟凸等新概念，给出了集合函数多目标规划问题有效解的一阶充分条件，弱有效解的阶必要条件，弱有交解的一阶必要条件以及强有效解的一阶充分条件。     

基于二层规划模型满意度的委托代理问题求解

主要基于二层规划问题研究非对称信息条件下的委托代理问题,分析委托人如何设计最优激励契约来与代理人达到双赢决策策略.具体思路如下:考虑到代理人对自身利益的追求,将代理人的效益函数与委托人的效益函数放置同一层构成双目标上层问题进行讨论,然后从不同权重条件下的弱有效解序列中寻找使得委托人和代理人都可接受的满意契约,实现双方共赢的目的.     

关于随机过程一阶概率分布函数的遍历性

本文提出了随机过程的一阶概率分布函数具有遍历性的一个充分必要条件(定理1和推论1),并在较弱条件下,对一般的关于随机变量函数分布定理作了进一步的推广(定理2)。利用这些结果,我们讨论了随机初相位周期过程的一阶分布函数的遍历性(推论2),最后,作为应用,我们用具有一维均匀分布的随机过程构造了一种白噪音模型。     

稳定分布的参数估计

由于金融数据经常具有“高峰厚尾”现象，所以用稳定分布去拟合，但由于稳定分布没有密度函数显式，而且可能一阶矩或二阶矩又不存在，因此稳定分布的参数估计问题用经典方法很难处理，本文利用类拟Duffie和Singleton(1993)的模拟矩法（SMM）的想法，构造了一种新的参数估计方法，并得到该估计的强相合性结果，最后举了一个实际的例子，分析了深圳成分指数的情况。     

近似已知函数的求导方法

1 引言 在实际问题中会遇到求近似已知函数的微商，这是一个典型的不适定问题[1-2]，即函数的一个微小的扰动会使得导数值有巨大的变化，因此求导数是相当不稳定的，对这类问题需要用特殊的方法。     

微积分在Cobb-Douglas生产函数混沌动力学系统中的应用

Cobb Douglas生产函数的非线性动态混沌性决定它在解决复杂经济现象 ,尤其是在我国市场经济的运行和管理中起着重要作用。本文用微积分方法研究这一模型系统的几个基本问题 ;用不定积分、中心差商方法分别给出用来刻画 Cobb Douglas生产函数动态过程的哈维尔摩增长模型连续解与离散解的简捷证明 ;用求极值的导数法则给出 Cobb Douglas生产函数混沌动力学系统成立的条件 ;用台劳公式和最小二乘法 (简称 n阶方法 )给出模型参数估计一种有效实用方法。最后用一个实例验证了n阶方法估计参数的实用性有效性。型如Yt=ANat (1 )为 Cobb Dougla…     

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The additive model is a more flexible nonparametric statistical model which allows a data-analytic transform of the covariates.When the number of covariates is big and grows exponentially with the sample size the urgent issue is to reduce dimensionality from high to a moderate scale. In this paper, we propose and investigate marginal empirical likelihood screening methods in ultra-high dimensional additive models. The proposed nonparametric screening method selects variables by ranking a measure of the marginal empirical likelihood ratio evaluated at zero to differentiate contributions of each covariate given to a response variable. We show that, under some mild technical conditions, the proposed marginal empirical likelihood screening methods have a sure screening property and the extent to which the dimensionality can be reduced is also explicitly quantified. We also propose a data-driven thresholding and an iterative marginal empirical likelihood methods to enhance the finite sample performance for fitting sparse additive models. Simulation results and real data analysis demonstrate the proposed methods work competitively and performs better than competitive methods in error of a heteroscedastic case.     

果蝇优化最小二乘支持向量机混合预测模型—以我国物流需求量预测为例

为解决最小二乘支持向量机参数设置的盲目性,利用果蝇优化算法对其参数进行优化选择,进而构建了果蝇优化最小二乘支持向量机混合预测模型.以我国物流需求量预测为例,验证了该模型的可行性和有效性.实例验证结果表明:与单一最小二乘支持向量机和模拟退火算法优化最小二乘支持向量机预测模型相比,该模型不仅能够有效选择参数值,而且预测精度更高.     

Empirical likelihood inference for linear transformation models

Empirical likelihood inference is developed for censored survival data under the linear transformation models, which generalize Cox's [Regression models and life tables (with Discussion), J. Roy. Statist. Soc. Ser. B 34 (1972) 187-220] proportional hazards model. We show that the limiting distribution of the empirical likelihood ratio is a weighted sum of standard chi-squared distribution. Empirical likelihood ratio tests for the regression parameters with and without covariate adjustments are also derived. Simulation studies suggest that the empirical likelihood ratio tests are more accurate (under the null hypothesis) and powerful (under the alternative hypothesis) than the normal approximation based tests of Chen et al. [Semiparametric of transformation models with censored data, Biometrika 89 (2002) 659-668] when the model is different from the proportional hazards model and the proportion of censoring is high.     

Experimentation and Learning in Repeated Cooperation

We study an agency model, in which the principal has only incomplete information about the agent's preferences, in a dynamic setting. Through repeated interaction with the agent, the principal learns about the agent's preferences and can thus adjust the inventive system. In a dynamic computational model, we compare different learning strategies of the principal when facing different types of agents. The results indicate that better learning of preferences can improve the situation of both parties, but the learning process is rather sensitive to random disturbances.     

Inferences based on a bivariate distribution with von Mises marginals

There is very little literature concerning modeling the correlation between paired angular observations. We propose a bivariate model with von Mises marginal distributions. An algorithm for generating bivariate angles from this von Mises distribution is given. Maximum likelihood estimation is then addressed. We also develop a likelihood ratio test for independence in paired circular data. Application of the procedures to paired wind directions is illustrated. Employing simulation, using the proposed model, we compare the power of the likelihood ratio test with six existing tests of independence.     

Empirical Likelihood Inference for AR（p） Model

In this article we study the empirical likelihood inference for AR（p） model. We propose the moment restrictions, by which we get the empirical likelihood estimator of the model parametric, and we also propose an empirical log-likelihood ratio base on this estimator. Our result shows that the EL estimator is asymptotically normal, and the empirical log-likelihood ratio is proved to be asymptotically standard chi-squared.     

MA$(q)$模型的经验似然推断

In this article we study the empirical likelihood inference for MA（q） model. We propose the moment restrictions, by which we get the empirical likelihood estimator of the model parameter, and we also propose an empirical log-likelihood ratio based on this estimator. Our result shows that the EL estimator is asymptotically normal, and the empirical log-likelihood ratio is proved to be asymptotical standard chi-square distribution.     

Comparison of two variances under inequality constraints by using empirical likelihood method

In this article, the empirical likelihood introduced by Owen Biometrika, 75, 237-249 （1988） is applied to test the variances of two populations under inequality constraints on the parameter space. One reason that we do the research is because many literatures in this area are limited to testing the mean of one population or means of more than one populations; the other but much more important reason is： even if two or more populations are considered, the parameter space is always without constraint. In reality, parameter space with some kind of constraints can be met everywhere. Nuisance parameter is unavoidable in this case and makes the estimators unstable. Therefore the analysis on it becomes rather complicated. We focus our work on the relatively complicated testing issue over two variances under inequality constraints, leaving the issue over two means to be its simple ratiocination. We prove that the limiting distribution of the empirical likelihood ratio test statistic is either a single chi-square distribution or the mixture of two equally weighted chi-square distributions.     

Pseudo-moderate deviations in the euler method for real diffusion process

We consider the problem of the non-sequential detection of a change in the drift coefficient of a stochastic differential equation, when a misspecified model is used. We formulate the generalized likelihood ratio (GLR) test for this problem, and we study the behaviour of the associated error probabilities (false alarm and nodetection) in the small noise asymptotics. We obtain the following robustness result: even though a wrong model is used, the error probabilities go to zero with exponential rate, and the maximum likelihood estimator (MLE) of the change time is consistent, provided the change to be detected is larger (in some sense) than the misspecification error. We give also computable bounds for selecting the threshold of the test so as to achieve these exponential rates.     

DSLC-FOA : Improved fruit fly optimization algorithm for application to structural engineering design optimization problems

In this study, we propose an improved fruit fly optimization algorithm (FOA) based on linear diminishing step and logistic chaos mapping (named DSLC-FOA) for solving benchmark function unconstrained optimization problems and constrained structural engineering design optimization problems. Based on comparisons with genetic algorithm, particle swarm optimization, FOA, LGMS -FOA, and chaotic FOA methods, we demonstrated that DSLC-FOA performed better at searching for the optimal solutions of four typical benchmark functions. The approximate optimal results were obtained using DSLC-FOA for three structural engineering design optimization problems as examples of applications. The numerical results demonstrated that the proposed DSLC-FOA algorithm is superior to the basic FOA and other metaheuristic or deterministic methods.