非线性m点奇异边值问题的正解

在超线性和次线性条件下利用锥上不动点定理得到了一类m点奇异边值问题正解的存在性结果.     

介质切伦可夫脉塞的线性理论

利用自洽线性场理论，在有无纵向引导磁场的情况下，分别分析了电子的三维扰动速度和三维扰动电流密度对介质切伦可夫脉塞注波互作用的影响，导出了其色散方程和注波工作用的同步条件，所得方程清楚地表明，其互作用不稳定性是由介质简慢波波导中的ＴＭ模与电子注模通过电子注耦合所致，并求得了电子注引起的频偏和波的增长率。     

Sensitivity analysis in maximum likelihood factor analysis

The present paper deals with sensitivity analysis in maximum likelihood factor analysis. To investigate the influence of a small change of data we derive theoretical influence functions I(x; LL^T ) and I(x; Δ) for a common variance matrix T= LL^T and a unique variance matrix Δ respectively. Numerical examples are shown to illustrate our procedure.     

Marginal-frechet-bounds for multidimensional distribution functions

In this paper for an n-dimensional distribution function F and each natural number m, m<n, n-dimensional distribution functions F^l(m) and F^y(m) are constructed such that the m-dimensional marginal distributions are the same as for F and the following inequality holds F^l(m) ≦ F ≦ F^u(m)     

Sensitivity analysis in principal component analysis:influence on the subspace spanned by principal components.

The problem of detecting influential observations in principalcomponent analysis was discussed by several authors. Radhakrishnan and kshirsagar ( 1981 ), Critchley ( 1985 ), jolliffe ( 1986 )among others discussed this topicby using the influence functions I(X;θ_s)and I(X;V_s)of eigenvalues and eigenvectors, which wwere derived under the assumption that the eigenvalues of interest were simple. In this paper we propose the influence functionsI(X;∑^q _s=1θ_sV_sV_s ^T)and I(x;∑^q _s=1V_sV_s ^t)(q<p;p:number of variables) to investigate the influence onthe subspace spanned by principal components. These influence functions are applicable not only to the case where the edigenvalues of interst are all simple but also to the case where there are some multiple eigenvalues among those of interest.     

On the independence between two generalized second degree polynomial statistics and their covariance

The condition of independence between two generalized second degree polynomial statistics is worked out in this paper under the assumption that the covariance matrix of the underlying normal distribution may be singular. The covariance between two such forms is also worked out without the assumption of normality. The results are indicated in compact matrix form.     

Sensitivity analysis and related analyses: A review of some statistical techniques

This paper reviews five related types of analysis, namely (i) sensitivity or what-if analysis, (ii) uncertainty or risk analysis, (iii) screening, (iv) validation, and (v) optimization. The main questions are: when should which type of analysis be applied; which statistical techniques may then be used? This paper claims that the proper sequence to follow in the evaluation of simulation models is as follows. 1) Validation, in which the availability of data on the real system determines which type of statistical technique to use for validation. 2) Screening: in the simulation‘s pilot phase the really important inputs can be identified through a novel technique, called sequential bifurcation, which uses aggregation and sequential experimentation. 3) Sensitivity analysis: the really important inputs should be subjected to a more detailed analysis, which includes interactions between these inputs; relevant statistical techniques are design of experiments (DOE) and regression analysis. 4) Uncertainty analysis: the important environmental inputs may have values that are not precisely known, so the uncertainties of the model outputs that result from the uncertainties in these model inputs should be quantified; relevant techniques are the Monte Carlo method and Latin hypercube sampling. 5) Optimization: the policy variables should be controlled; a relevant technique is Response Surface Methodology (RSM), which combines DOE, regression analysis, and steepest-ascent hill-climbing. The recommended sequence implies that sensitivity analysis procede uncertainty analysis. Several case studies for each phase are briefly discussed in this paper.     

量刑情节单复数问题研究

量刑情节单复数的判断应当围绕具体案件进行,其要旨首先在于量刑情节的界定问题。派生的构成要件不应作为量刑情节;在综合性构成要件中,只能将其中之一选定为构成要件,其他剩余的要件则转换为量刑情节。当量刑情节之间呈现结合关系、包容关系或竞合关系时,应当按单一情节对待;在一人犯数罪时,行为人的一次立功只能视为一个量刑情节,而行为人的未成年人身份及累犯情节应当按复数量刑情节对待。     

Improved ridge estimators in a linear regression model

In this paper, the notion of the improved ridge estimator (IRE) is put forward in the linear regression model y=X β+e. The problem arises if augmenting the equation 0=c′α+ε instead of 0=C α+? to the model. Three special IREs are considered and studied under the mean-squared error criterion and the prediction error sum of squares criterion. The simulations demonstrate that the proposed estimators are effective and recommendable, especially when multicollinearity is severe.