支持MLS的多层次嵌入式高可信软件架构

为增强安全关键系统的高可信能力,在分析高可信保障机制现状的基础上,提出了一种多层次的高可信软件架构。该架构采用时空分离思想、虚拟机技术,为基于MLS的嵌入式安全关键系统提供了一种整体解决方案。基于该架构,研究了多层次的安全和防危策略管理方法、信息流控制机制、可信软件的评估和认证方法,为安全关键嵌入式系统提供可认证的安全服务。     

复杂系统质量生存决策的模型研究

在复杂经济系统的生存决策模型的基础上加入期望约束,提出并建立了一个基于资源、惯性和期望约束的质量生存决策的三维基本模型.由于决策系统的质量生存性由质量生存核维持,但在一般情况下得不到质量生存核的解析解,因此对其大小、形状、位置进行了理论分析,并根据理论分析结果,按照不同质量生存特性将三维决策空间进行了定性区域划分,最后用一个仿真实例进行了简单验证.     

两类广义卷积

把定义在半直线Ｒ＋＝［０，∞）上概率测度的广义卷积推广到了紧空间Ｒ＋＝［０，∞］上．讨论了与连续性、单位元有关的性质．根据单位元的可能性，导出了两类广义卷积，研究了它们之间的关系．比较了本文所用的推广方法与Ｕｒｂａｎｉｋ的推广方法之间的联系．     

“翻译腔”例析

因理解之误和表达之误所致,"翻译腔"表现各异.作者认为,在英汉翻译中,只有努力钻研原作,充分理解原文的深层含义,熟悉和掌握英汉语言的主要差异,表达符合译语语言习惯,才能使译文流畅自如,提高可接受性,消除"翻译腔".     

两度对话翻译教学模式的产生与阐释

笔者先将翻译流程设计在一个由原语形式到原型,再由原型到译语形式的折线之内,然后又将这一折线囊括在一个语境体系之中.其操作方式是"对话".其对话结果是通过语内和语际两度转换在语境体系的制约中实现的.     

Kernel estimation of a distribution function

A distribution function is estimated by a kernel method with

a poinrwise mean squared error criterion at a point x. Relation- ships between the mean squared error, the point x, the sample size and the required kernel smoothing parazeter are investigated for several distributions treated by Azzaiini (1981). In particular it is noted that at a centre of symmetry or near a mode of the distribution the kernei method breaks down. Point- wise estimation of a distribution function is motivated as a more useful technique than a reference range for preliminary medical diagnosis.     

Kernel Density Estimation with Generalized Binning

We propose kernel density estimators based on prebinned data. We use generalized binning schemes based on the quantiles points of a certain auxiliary distribution function. Therein the uniform distribution corresponds to usual binning. The statistical accuracy of the resulting kernel estimators is studied, i.e. we derive mean squared error results for the closeness of these estimators to both the true function and the kernel estimator based on the original data set. Our results show the influence of the choice of the auxiliary density on the binned kernel estimators and they reveal that non-uniform binning can be worthwhile.     

Testing lack of fit of regression models under heteroscedasticity

A test is proposed for assessing the lack of fit of heteroscedastic nonlinear regression models that is based on comparison of nonparametric kernel and parametric fits. A data-driven method is proposed for bandwidth selection using the asymptotically optimal bandwidth of the parametric null model which leads to a test that has a limiting normal distribution under the null hypothesis and is consistent against any fixed alternative. The resulting test is applied to the problem of testing the lack of fit of a generalized linear model.     

Pointwise and uniform convergence of multivariate kernel density estimators using random bandwidths

We obtain the rates of pointwise and uniform convergence of multivariate kernel density estimators using a random bandwidth vector obtained by some data-based algorithm. We are able to obtain faster rate for pointwise convergence. The uniform convergence rate is obtained under some moment condition on the marginal distribution. The rates are obtained under i.i.d. and strongly mixing type dependence assumptions.     

A test of normality using nonparametrlic residuals

In this paper, we develop a test of the normality assumption of the errors using the residuals from a nonparametric kernel regression. Contrary to the existing tests based on the residuals from a parametric regression, our test is thus robust to misspecification of the regression function. The test statistic proposed here is a Bera-Jarque type test of skewness and kurtosis. We show that the test statistic has the usual x²(2) limit distribution under the null hypothesis. In contrast to the results of Rilstone (1992), we provide a set of primitive assumptions that allow weakly dependent observations and data dependent bandwidth parameters. We also establish consistency property of the test. Monte Carlo experiments show that our test has reasonably good size and power performance in small samples and perfornu better than some of the alternative tests in various situations.