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.     

On a Relation between Principal Components and Regression Analysis

A regression approach to principal component analysis is presented in this note. We provide an alternative interpretation of principal components that illustrates the relation between the extra sum of squares in regression analysis and the eigenvalues associated with the principal components.     

全局主成分法在土地集约利用评价中应用——以中部地区为例

在构建评价指标体系基础上,运用基于三维立体时序全局表的全局主成分法对1997￣2005年东部、中部、西部、东北地区原始数据进行分析,对比分析得出中国中部地区的土地集约利用水平变化规律。结果表明:全局主成分法的适用性广;近9年来中部地区土地集约利用水平在不断提高;中部地区土地集约利用水平与东部和西部、东北地区比较呈现两极变化趋势。     

中药有效成分的分析方法及应用

对中药有效成分分析的方法进行了综述,阐述了其特点、应用范围及研究现状,旨在为中药有效成分的分析工作提供参考。     

Effects of non-normality on the power function in a one-way random model

The effects of non-normality on type-I and type-II errors in a one-way random model are investigated for moderate departures from normality. It is found that the probabilities of both errors are more sensitive to the kurtosis of between group effects than that of within group effects.     

Unconstrained parametrizations for variance-covariance matrices

The estimation of variance-covariance matrices through optimization of an objective function, such as a log-likelihood function, is usually a difficult numerical problem. Since the estimates should be positive semi-definite matrices, we must use constrained optimization, or employ a parametrization that enforces this condition. We describe here five different parametrizations for variance-covariance matrices that ensure positive definiteness, thus leaving the estimation problem unconstrained. We compare the parametrizations based on their computational efficiency and statistical interpretability. The results described here are particularly useful in maximum likelihood and restricted maximum likelihood estimation in linear and non-linear mixed-effects models, but are also applicable to other areas of statistics.     

Generalized linear models with functional predictors

Summary. We present a technique for extending generalized linear models to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal components can be used to gain insight into the relationship between the response and functional predictors. Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems.     

内幕交易民事责任及诉讼机制探讨

不同类型的内幕交易受害人取诉权的条件和方式不尽相同。内幕交易民事责任的各个构成要件都有其特定内涵。内幕交易民事诉讼机制由代表人诉讼和股东派生诉讼所构成。     

Slack-variable models versus Scheffé's mixture models

Slack-variable models are compared against Scheffé's polynomial model for mixture experiments. The notion of model equivalence and the use of various diagnostic measures provide effective tools in making such comparisons, particularly when the experimental region is highly constrained. It is demonstrated that the choice of the best fitting model, through variable selection, depends on which mixture component is selected as a slack variable, and on the size of the fitted model. In addition, the equivalence of two well-known representations of a complete mixture model is shown to be valid. Two numerical examples are presented.     

论“温州模式”中的人口分布效应——浙江省地级市经济块状集聚的人口空间布局效应

温州是我国外来务工人员最为集中的几个城市之一,但是由学理分析而得出的结论却是温州不仅不存在外来务工人员的就业空间,而且还要从其内部向外大量迁移人口。这种现实与理论的矛盾构成的“温州悖论”,其实可以通过“行政区经济”和“经济区经济”的分析框架而得到回归,由此也就说明了经济发展过程中块状经济对人口分布所具有的巨大效应。