《淮南子》二论

秉承于先秦道家的《淮南子》,无论在哲学思想上,还是在音乐理论上,均是对道家思想的继承,并有进一步的发展,主要体现在"神主形从"说及其对音乐形神的论述中.     

Dimensionality reduction approach to multivariate prediction

The authors consider dimensionality reduction methods used for prediction, such as reduced rank regression, principal component regression and partial least squares. They show how it is possible to obtain intermediate solutions by estimating simultaneously the latent variables for the predictors and for the responses. They obtain a continuum of solutions that goes from reduced rank regression to principal component regression via maximum likelihood and least squares estimation. Different solutions are compared using simulated and real data.     

On a statistic useful in dimensionality reduction in multivariable linear stochastic system

In this paper we study the sampling properties of a test statistic which has important applications in the area of linear stochastic control systems with multi-inputs and multi-outputs. The statistic is the ratio of a partial sum of the eigenvalues of a sample covariance matrix and its trace. It turns out that using a method due to Sugiura we may derive a useful approximation for its distribution up to and including terms of order l/n, where n denotes the appropriate size. Numerical illustrations using real data are given.     

Shrinkage Structure of Partial Least Squares

Partial least squares regression (PLS) is one method to estimate parameters in a linear model when predictor variables are nearly collinear. One way to characterize PLS is in terms of the scaling (shrinkage or expansion) along each eigenvector of the predictor correlation matrix. This characterization is useful in providing a link between PLS and other shrinkage estimators, such as principal components regression (PCR) and ridge regression (RR), thus facilitating a direct comparison of PLS with these methods. This paper gives a detailed analysis of the shrinkage structure of PLS, and several new results are presented regarding the nature and extent of shrinkage.     

A clustering approach to interpretable principal components

A new method for constructing interpretable principal components is proposed. The method first clusters the variables, and then interpretable (sparse) components are constructed from the correlation matrices of the clustered variables. For the first step of the method, a new weighted-variances method for clustering variables is proposed. It reflects the nature of the problem that the interpretable components should maximize the explained variance and thus provide sparse dimension reduction. An important feature of the new clustering procedure is that the optimal number of clusters (and components) can be determined in a non-subjective manner. The new method is illustrated using well-known simulated and real data sets. It clearly outperforms many existing methods for sparse principal component analysis in terms of both explained variance and sparseness.     

也谈新时期群众工作的重点和难点

当前,群众工作面临着许多新挑战,出现了许多新问题,这不仅是个理论问题,更是个实践问题。为做好新时期、新特点下的群众工作,不断提高群众工作的有效性,我们必须不断探索群众工作的新方法：要加大对党的理论和重大方针政策的解读力度;要增强理想信念的亲和力和吸引力;要不断提高执政理性的说服力;要提升党员干部践行＂三种力＂的能力。     

再析表见代理性质与被代理人过失

表见代理虽发生有权代理的效果 ,但将其归为有权代理却有诸多弊端 ,将其认定为无权代理则更具有合理性。对于表见代理是否应以被代理人过失为要件 ,应视构成表见代理的各种情形而定。     

不对称信息下风险投资家与风险企业家之间的委托代理问题研究

风险投资体系以多重激励和不对称信息为特征,风险投资的三方利益主体之间存在着双重委托-代理关系,有效控制代理风险是风险投资高效运作的基本前提。本文基于信息经济学原理,分析了风险投资家与风险企业家之间代理风险的成因,并从委托人的角度讨论了风险治理的原则和投资契约的构造。     

我国居民生活质量评价指标与综合评价研究

生活质量不仅包含了福利的经济内函 ,还包含了像健康、教育、贫困、社会环境和自然环境质量等影响人们生活条件的诸多非经济要素。文章从收入状况、居民消费、社会安全、教育状况、健康状况、资源与环境、城市环境和社会服务八个方面提出了我国居民生活质量评价指标体系。现行生活质量综合评价方法不仅会受到主观因素的影响 ,而且无法剔除指标间的重叠信息 ,采用主成份分析法对居民生活质量进行综合评价恰好能够解决这些问题 ,从而可以提高其结果的真实性。     

Summarizing the Effect of a Wide Array of Amenity Measures into Simple Components

A significant issue existing within the rural economic development literature revolves around the difficulty with sorting out the controversy of the effects of amenity activities on rural economic growth. This problem is due to the different ways amenity attributes are linked to regional economic performance. Numerous researchers utilize principal component analysis to compress groups of variables that describe attributes of natural-based amenity and quality of life into scalar measures. While principal components are good at reducing a collection of variables into single measures, they often lack interpretability because they define some abstract scores which are often not meaningful or not well interpretable in practice. We apply the simple component analysis suggested by Rousson and Gasser (2004, Applied Statistics 53, 539–555) to summarize the information in groups of variables into a limited number of simple components and improve interpretability at a modest loss of optimality. Simple components allow us to identify and interpret the effect of attributes that most influence regional economic performance so as to gain better insight into policies to preserve and advance those attributes. The same methodology is appropriate for any social science discipline when there is a need to replace a larger number of multiple indicator measurements with a smaller set.