从功能需求看西方社会学理论研究视角的转换

可以用功能取向为线索梳理西方社会学理论发展的历史脉络.西方社会学理论研究视角的转换是随时代的变迁而不断转向的,迄今为止,西方社会学理论发展大致可以分为萌芽、成熟、发展和放飞四个阶段.与时代接轨,与现实相连是社会学理论生存和发展的惟一条件.     

Modeling Dependence in High Dimensions With Factor Copulas

This article presents flexible new models for the dependence structure, or copula, of economic variables based on a latent factor structure. The proposed models are particularly attractive for relatively high-dimensional applications, involving 50 or more variables, and can be combined with semiparametric marginal distributions to obtain flexible multivariate distributions. Factor copulas generally lack a closed-form density, but we obtain analytical results for the implied tail dependence using extreme value theory, and we verify that simulation-based estimation using rank statistics is reliable even in high dimensions. We consider “scree” plots to aid the choice of the number of factors in the model. The model is applied to daily returns on all 100 constituents of the S&P 100 index, and we find significant evidence of tail dependence, heterogeneous dependence, and asymmetric dependence, with dependence being stronger in crashes than in booms. We also show that factor copula models provide superior estimates of some measures of systemic risk. Supplementary materials for this article are available online.     

A nonparametric inverse probability weighted estimation for functional data with missing response data at random

This paper considers the nonparametric inverse probability weighted estimation for functional data with missing response data at random. Under mild conditions, the asymptotic properties of the proposed estimation method are established. Based on the resampling method, the estimation of the asymptotic variance of the proposed estimator is obtained. Finally, the finite sample properties of the proposed estimation method are investigated via Monte Carlo simulation studies. A real data analysis is given to illustrate the use of the proposed method.     

论学分制下的大学英语专业语法教学——基于浙江海洋学院语法课程的一份调查

近几年,随着高校学分制的推行,英语专业的语法课程教学时数不断减少。如何在新的教学环境下发展英语语法课程成了关注的焦点。本文通过对浙江海洋学院英语专业学分制后的语法课程调查,发现了目前英语语法课程实效低下的问题。在具体分析了英语语法课程存在的问题及原因的基础上,提出了在课程结构,教学方法,教材建设等方面发展语法课程的一些措施,以期满足学生和教学的实际需求,提高英语语法教学的效果。     

Multivariate trimmed means based on the Tukey depth

In univariate statistics, the trimmed mean has long been regarded as a robust and efficient alternative to the sample mean. A multivariate analogue calls for a notion of trimmed region around the center of the sample. Using Tukey's depth to achieve this goal, this paper investigates two types of multivariate trimmed means obtained by averaging over the trimmed region in two different ways. For both trimmed means, conditions ensuring asymptotic normality are obtained; in this respect, one of the main features of the paper is the systematic use of Hadamard derivatives and empirical processes methods to derive the central limit theorems. Asymptotic efficiency relative to the sample mean as well as breakdown point are also studied. The results provide convincing evidence that these location estimators have nice asymptotic behavior and possess highly desirable finite-sample robustness properties; furthermore, relative to the sample mean, both of them can in some situations be highly efficient for dimensions between 2 and 10.     

区域工业产业发展系统动力学建模研究——以广州市为例

运用系统动力学（SD）相关理论对区域工业产业发展系统的有效运行进行建模，探索一种从定性与定量相结合的角度反映区域工业产业发展系统运行机制的方法，观测其行为特性，找到起主要作用的反馈回路和政策的杠杆作用点，为政府制定工业产业发展政策提供依据。以广州市为例，结合国际和国内区域工业产业发展的相关理论，将广州市工业产业发展系统划分为工业生产、区域技术创新环境、区域经济环境3个子系统，通过对区域产业发展系统进行严谨的系统因果反馈分析，从微观结构入手考察区域产业发展的影响因素与制约条件，寻找其根源，并在此基础上提出相应政策建议。     

Genetic Algorithm in the Wavelet Domain for Large p Small n Regression

Many areas of statistical modeling are plagued by the “curse of dimensionality,” in which there are more variables than observations. This is especially true when developing functional regression models where the independent dataset is some type of spectral decomposition, such as data from near-infrared spectroscopy. While we could develop a very complex model by simply taking enough samples (such that n > p), this could prove impossible or prohibitively expensive. In addition, a regression model developed like this could turn out to be highly inefficient, as spectral data usually exhibit high multicollinearity. In this article, we propose a two-part algorithm for selecting an effective and efficient functional regression model. Our algorithm begins by evaluating a subset of discrete wavelet transformations, allowing for variation in both wavelet and filter number. Next, we perform an intermediate processing step to remove variables with low correlation to the response data. Finally, we use the genetic algorithm to perform a stochastic search through the subset regression model space, driven by an information-theoretic objective function. We allow our algorithm to develop the regression model for each response variable independently, so as to optimally model each variable. We demonstrate our method on the familiar biscuit dough dataset, which has been used in a similar context by several researchers. Our results demonstrate both the flexibility and the power of our algorithm. For each response variable, a different subset model is selected, and different wavelet transformations are used. The models developed by our algorithm show an improvement, as measured by lower mean error, over results in the published literature.     

Nonparametric regression for functional data: Automatic smoothing parameter selection

We study regression estimation when the explanatory variable is functional. Nonparametric estimates of the regression operator have been recently introduced. They depend on a smoothing factor which controls its behavior, and the aim of our work is to construct some data-driven criterion for choosing this smoothing parameter. The criterion can be formulated in terms of a functional version of cross-validation ideas. Under mild assumptions on the unknown regression operator, it is seen that this rule is asymptotically optimal. As by-products of this result, we state some asymptotic equivalences for several measures of accuracy for nonparametric estimate of the regression operator. We also present general inequalities for bounding moments of random sums involving functional variables. Finally, a short simulation study is carried out to illustrate the behavior of our method for finite samples.     

《马氏文通》字类划分标准辨正

说《马氏文通》划分字类的标准是"词汇意义"是不妥的,《马氏文通》是依据字的"语法意义"划分字类的。它所运用的划分字类的标准,被后来人继承并加以完善。它所建立的汉语词类系统,包括实字五类:名字、代字、静字、动字、状字;虚字四类:介字、连字、助字、叹字,大体上是合理的,发展到现在,除了把"字"改为"词"和做一些局部的小调整外,并没有多少大的变化。     

Two-step estimation of functional linear models with applications to longitudinal data

Functional linear models are useful in longitudinal data analysis. They include many classical and recently proposed statistical models for longitudinal data and other functional data. Recently, smoothing spline and kernel methods have been proposed for estimating their coefficient functions nonparametrically but these methods are either intensive in computation or inefficient in performance. To overcome these drawbacks, in this paper, a simple and powerful two-step alternative is proposed. In particular, the implementation of the proposed approach via local polynomial smoothing is discussed. Methods for estimating standard deviations of estimated coefficient functions are also proposed. Some asymptotic results for the local polynomial estimators are established. Two longitudinal data sets, one of which involves time-dependent covariates, are used to demonstrate the approach proposed. Simulation studies show that our two-step approach improves the kernel method proposed by Hoover and co-workers in several aspects such as accuracy, computational time and visual appeal of the estimators.