风险偏好与属性约简在决策问题中的应用研究

将决策者风险偏好与属性约简算法应用到具有大量属性的决策问题,提出先对决策者分类再进行决策的策略． 根据决策者的风险偏好特征将其分为风险规避型、风险中立型、风险偏好型,针对不同类型的决策者分别采取相应的算法,提取有效属性并利用有效属性进行决策; 针对不同风险偏好的决策者,提出相应的风险偏好预期规则; 提出基于优势关系辨析矩阵的属性赋权算法及基于属性值优势度矩阵的进行信息融合与排序算法; 最后通过属性值为实数与区间数的两个实际案例表明该算法的科学合理性．     

A spatial Bayesian semiparametric mixture model for positive definite matrices with applications in diffusion tensor imaging

Studies on diffusion tensor imaging (DTI) quantify the diffusion of water molecules in a brain voxel using an estimated 3 × 3 symmetric positive definite (p.d.) diffusion tensor matrix. Due to the challenges associated with modelling matrix‐variate responses, the voxel‐level DTI data are usually summarized by univariate quantities, such as fractional anisotropy. This approach leads to evident loss of information. Furthermore, DTI analyses often ignore the spatial association among neighbouring voxels, leading to imprecise estimates. Although the spatial modelling literature is rich, modelling spatially dependent p.d. matrices is challenging. To mitigate these issues, we propose a matrix‐variate Bayesian semiparametric mixture model, where the p.d. matrices are distributed as a mixture of inverse Wishart distributions, with the spatial dependence captured by a Markov model for the mixture component labels. Related Bayesian computing is facilitated by conjugacy results and use of the double Metropolis–Hastings algorithm. Our simulation study shows that the proposed method is more powerful than competing non‐spatial methods. We also apply our method to investigate the effect of cocaine use on brain microstructure. By extending spatial statistics to matrix‐variate data, we contribute to providing a novel and computationally tractable inferential tool for DTI analysis.     

结构和内容联合提取的XML网页分类研究

针对XML网页特点,重点研究了XML文档结构和内容特征的提取方法,提出了一种基于频繁结构层次空间模型的联合特征提取策略,并给出了结构特征权重和关键词出现的位置及频度权重的计算公式,并根据计算结果提取XML网页特征矩阵,分别就结构、内容联合提取三种情况进行分类测试,通过ROSSETA系统,利用粗糙集优越的属性约简构造文本分类系统,实现XML文档分类。实验表明,该方法分类准确度较高,计算量较小。     

混合核函数对支持向量机分类性能的改进

通过对核矩阵的计算和研究,从理论上对常用的核函数进行了评估.在此基础上,通过实验仿真证实了通过优选后的核函数所组成的混合核函数对支持向量机分类性能的改善,为核函数的选择提供了参考.     

On disjoint (3t, 3, 1) cyclic difference families

We give a new and easier proof of the existence of a disjoint (3t  , 3, 1) cyclic difference family for every t>3$t>3$, first proved by Dinitz and Shalaby (2002). Our purely theoretical construction is still elementary but simpler and does not need to be checked by computer.     

Riordan matrices and higher-dimensional lattice walks

An algebraic combinatorial method is used to count higher-dimensional lattice walks in Z^m${\mathbb{Z}}^m$ that are of length n ending at height k. As a consequence of using the method, Sands’ two-dimensional lattice walk counting problem is generalized to higher dimensions. In addition to Sands’ problem, another subclass of higher-dimensional lattice walks is also counted. Catalan type solutions are obtained and the first moments of the walks are computed. The first moments are then used to compute the average heights of the walks. Asymptotic estimates are also given.     

Estimation of covariance matrix via the sparse Cholesky factor with lasso

In this paper, we discuss a parsimonious approach to estimation of high-dimensional covariance matrices via the modified Cholesky decomposition with lasso. Two different methods are proposed. They are the equi-angular and equi-sparse methods. We use simulation to compare the performance of the proposed methods with others available in the literature, including the sample covariance matrix, the banding method, and the L₁-penalized normal loglikelihood method. We then apply the proposed methods to a portfolio selection problem using 80 series of daily stock returns. To facilitate the use of lasso in high-dimensional time series analysis, we develop the dynamic weighted lasso (DWL) algorithm that extends the LARS-lasso algorithm. In particular, the proposed algorithm can efficiently update the lasso solution as new data become available. It can also add or remove explanatory variables. The entire solution path of the L₁-penalized normal loglikelihood method is also constructed.     

Error rates in classification consisting of discrete and continuous variables in the presence of covariates

We consider classifying an object based on mixed continuous and discrete variables between two populations. Mixed discrete and continuous covariates with identical means in both populations are amongst the variables. Under the location model with homogeneous location specific conditional dispersion matrices for both populations, the Bayes rule is given. Classification is implemented by a plug-in version of the Bayes rule with full covariate adjustment. An asymptotic expansion of the overall expected error of the procedure is derived. Our findings generalize several classical results.     

产业整合中的并购融资决策模型

我国在经济总量长期快速增长后,当前很多行业产能过剩且与国际同行业市场相比产业集中度较低,已步入一个产业整合期。文章利用基于方差分析的资本结构决策模型,研究了产业整合中并购融资决策问题,有效地权衡了风险与收益,使并购融资决策模型更富可操作性和实用广泛性,为产业整合中企业并购融资的最优资本结构决策提供了科学依据。      

投资项目风险的识别与测度

任何投资项目都蕴涵着一定的风险,有效地识别和测度风险有助于最优决策的制定。传统项目评估方法很大程度上忽略了不确定性因素的影响,而不确定性因素是导致风险的重要诱因,故需对传统项目评价中的风险测度方法进行创新。投资项目所面临的风险可分为系统和非系统风险,多层次风险指标体系可形成AHP递阶层次结构,引入定性分析与定量计算有机结合的AHP模型可对投资项目的风险进行有效识别和测度。