试论严复对中国近代文化的贡献

严复是我国近代伟大的启蒙思想家 ,他翻译《天演论》 ,对中西文化进行比较研究 ,提出中国近代教育思想 ,为中国近代文化的发展做出了贡献     

An alternative to farmer age as an indicator of life-cycle stage: The case for a farm family age index

In studies of farming, the age of the principal decision-maker (PDM) has been associated with numerous farm structural and managerial features and has been widely accepted as a good indicator of the influence of life-cycle factors on decision-making. As such, it has become an important aspect of many quantitative studies of agricultural change. However, contemporary studies of family farming demonstrate that the concept of a single PDM in family farms is becoming an anachronism as alternative enterprises, pluriactivity and the scale of family farms force more diffuse management/operating systems. This raises questions concerning whether the age of the PDM can still be taken as representative of farm structure, strategy or life-cycle stage? Using a study conducted in the Grampian Mountains region of Scotland in 2003 this note investigates the impact of using an alternative index—compiled by averaging the age of family members working on the farm. It suggests that PDM age is a relatively poor indicator of farm structural and managerial features compared to a family age index and calls for researchers to think about alternative approaches to measuring ‘age’ as an indicator.     

福建省新型工业化基础评价与比较分析

我们建立一套能够比较全面地反映一个地区新型工业化基础的评价指标体系,利用2002、2003年的统计数据,运用主成分分析法对包括福建省在内的我国26个省、市、自治区的新型工业化基础进行评价,从信息化程度、工业化程度、科技含量、经济效益、可持续发展等方面将福建省与其他省市进行比较分析,明确福建省的优势与不足,在此基础上提出福建省在推进新型工业化进程中应注意的问题。     

Principal curves revisited

A principal curve (Hastie and Stuetzle, 1989) is a smooth curve passing through the middle of a distribution or data cloud, and is a generalization of linear principal components. We give an alternative definition of a principal curve, based on a mixture model. Estimation is carried out through an EM algorithm. Some comparisons are made to the Hastie-Stuetzle definition.     

Basic aspects of evolution strategies

Evolution strategies (ESs) are a special class of probabilistic, direct, global optimization methods. They are similar to genetic algorithms but work in continuous spaces and have the additional capability of self-adapting their major strategy parameters. This paper presents the most important features of ESs, namely their self-adaptation, as well as their robustness and potential for parallelization which they share with other evolutionary algorithms.Besides the early (1 + 1)-ES and its underlying theoretical results, the modern ( + )-ES and (, )-ES are presented with special emphasis on the self-adaptation of strategy parameters, a mechanism which enables the algorithm to evolve not only the object variables but also the characteristics of the probability distributions of normally distributed mutations. The self-adaptation property of the algorithm is also illustrated by an experimental example.The robustness of ESs is demonstrated for noisy fitness evaluations and by its application to discrete optimization problems, namely the travelling salesman problem (TSP).Finally, the paper concludes by summarizing existing work and general possibilities regarding the parallelization of evolution strategies and evolutionary algorithms in general.     

An algorithm for computing principal points with respect to a loss function in the unidimensional case

In Flury (1990) the k principal points of a random vector X are defned as the points p(1),..., p(k) minimizing EX–p(i)²; i=1,..., k. We extend this concept to that of k principal points with respect to a loss function L, and present an algorithm for their computation in the univariate case.     

Diffusion Indexes With Sparse Loadings

The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs to be taken when choosing which variables to include in the model. A number of different approaches to determining these variables have been put forward. These are, however, often based on ad hoc procedures or abandon the underlying theoretical factor model. In this article, we will take a different approach to the problem by using the least absolute shrinkage and selection operator (LASSO) as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model that is better suited for forecasting compared to the traditional principal components (PC) approach. We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on U.S. macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find it to be an important alternative to PC. Supplementary materials for this article are available online.     

中国国民财富总量、结构及其演进模式研究——基于“自上而下-自下而上”测度范式

“财富-福利等式”表明国民财富与社会福利具有理论等价关系,是评估发展可持续性的最优指示器。我国国民财富研究相对滞后,总量测度与结构分析都亟待深化。本文基于“自上而下-自下而上”范式,依据国情优化关键参数的设定,改进了我国国民财富与分类资本估算,并剖析了省际国民财富结构以识别其演进模式。研究发现:由于资源禀赋与发展阶段有别,省际国民财富结构差异显著;随着经济发展水平提升,国民财富结构先由自然资本主导转向物质资本与人力资本共同主导,进而转变为人力资本主导,最终迈入社会资本主导。各地区应结合自身禀赋与发展阶段做好“投资组合管理”,以此来提升国民财富并优化其结构从而改善经济发展的持续性。     

基于fused惩罚的稀疏主成分分析

本文旨在研究基于fused惩罚的稀疏主成分分析方法，以适用于相邻变量之间高度相关甚至完全相等的数据情形。首先，从回归分析角度出发，提出一种求解稀疏主成分的简便思路，给出一种广义的稀疏主成分模型—— GSPCA模型及其求解算法，并证明在惩罚函数取1-范数时，该模型与现有的稀疏主成分模型——SPC模型的求解结果一致。其次，本文提出将fused惩罚与主成分分析相结合，得到一种fused稀疏主成分分析方法，并从惩罚性矩阵分解和回归分析两个角度，给出两种模型形式。在理论上证明了两种模型的求解结果是一致的，故将其统称为FSPCA模型。模拟实验显示，FSPCA模型在处理相邻变量之间高度相关甚至完全相等的数据集上的表现良好。最后，将FSPCA模型应用于手写数字识别，发现与SPC模型相比，FSPCA模型所提取的主成分具备更好的解释性，这使得该模型更具实用价值。     

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.