"男主女从":必须扬弃的等级观——《布洛陀经诗》的性别哲学研究之三

《布洛陀经诗》虽然包含"阴阳合德"等合理因素,但从其宗教神灵观的主要特征,以及在角色定位上的主导思想、在价值评价上的主流意识等方面来看,"男主女从"的等级特征都较为明显。这种等级观体现了壮族性别哲学和壮族传统文化的某些糟粕,是我们在寻求性别和谐发展之道时所必须给予剔除和扬弃的。     

信息化在“四化”发展中的贡献度研究——基于中国省际面板数据

构建“四化”发展水平指标体系,基于2006-2013年中国省际面板数据,运用全局主成分分析法得出“四化”发展水平得分,在此基础上,对“四化”耦合发展互动关系进行计量分析.结果显示:“四化”之间存在长期均衡的协整关系;信息化对于农业现代化边际贡献度为0.231 8,弱于工业化的边际贡献;信息化对于城镇化的边际贡献度为0.702 3,强于工业化对城镇化的贡献;信息化对于工业化的边际贡献不显著.基于实证分析结果,提出加强城镇信息化建设和提升城镇化人口素质的政策建议.     

基于财政竞争力分析的福建省经济发展策略研究

财政对经济增长起着不可替代的促进作用,而财政竞争力也是经济综合竞争力的重要组成部分,分析福建省各地市的财政竞争力以寻找经济发展策略,对促进福建省经济有重大意义。本文对各地市财政收支现状进行初步比较,构建财政竞争力三级指标体系以分析财政竞争力水平,同时利用主成分因子得分分析财政竞争力与经济发展之间的关系。研究结果表明,厦门、福州及泉州财政竞争力较强,以这三个地市为例,从财政竞争实力和竞争潜力两个方面提出进一步提升财政竞争力的建议。     

创意都市农业CSA经营模式研究

依据创意都市农业的内涵及特征,分析经营主体相关成分的行为偏好及参与程度,结合不同经营时期的目标值,确定不同的主体组合方式,即：经营初期选择“农户+消费者”“农户+合作社”模式;成熟期选择“农户+消费者”“农户+合作社+企业”“农户+合作社+企业+消费者”模式。不同的CSA经营模式选择对延伸农业产业链,促进农旅结合,实现乡村可持续发展具有指导意义。     

A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.     

On asymptotic distribution of prediction in functional linear regression

There has been substantial recent attention on problems involving a functional linear regression model with scalar response. Among them, there have been few works dealing with asymptotic distribution of prediction in functional linear regression models. In recent literature, the centeral limit theorem for prediction has been discussed, but the proof and conditions under which the random bias terms for a fixed predictor converge to zero have been ignored so that the impact of these terms on the convergence of the prediction has not been well understood. Clarifying the proof and conditions under which the bias terms converge to zero, we show that the asymptotic distribution of the prediction is normal. Furthermore, we have derived those results related to other terms that already obtained by others, under milder conditions. Finally, we conduct a simulation study to investigate performance of the asymptotic distribution under various parameter settings.     

基于主成分logistic回归模型的餐饮商务信息意识研究

数据挖掘技术日趋成熟,广泛应用于金融、地产、投资、评估等各个领域。数据挖掘技术亦可应用于餐饮业,为其经营决策做出分析。其他领域的数据挖掘的成功案例也可以引用到餐饮行业中。本文对餐饮人士在餐饮商务中是否具备信息意识、对信息意识的重视程度进行调查,利用数据挖掘中主成分分析和 logistic 回归处理和分析收集来的数据,从而说明数据挖掘在餐饮业管理中的重要意义,反映餐饮人士信息意识状况。利用数据挖掘技术对餐饮商务资讯进行管理势必提高企业效率和盈利水平,促进餐饮业健康稳定的发展。     

SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.     

Classical testing in functional linear models

We extend four tests common in classical regression – Wald, score, likelihood ratio and F tests – to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.     

The eigenstructure of block-structured correlation matrices and its implications for principal component analysis

Block-structured correlation matrices are correlation matrices in which the p variables are subdivided into homogeneous groups, with equal correlations for variables within each group, and equal correlations between any given pair of variables from different groups. Block-structured correlation matrices arise as approximations for certain data sets’ true correlation matrices. A block structure in a correlation matrix entails a certain number of properties regarding its eigendecomposition and, therefore, a principal component analysis of the underlying data. This paper explores these properties, both from an algebraic and a geometric perspective, and discusses their robustness. Suggestions are also made regarding the choice of variables to be subjected to a principal component analysis, when in the presence of (approximately) block-structured variables.