游仙诗特点及分类

游仙诗是我国古典诗歌的重要类别，由于儒家对“怪力乱神”的排斥，在某种意义上堪于水山、田园鼎足而立的游仙诗没有得到学界的足够重视，本就游仙诗的特点进行了研究并提出分类。     

重新认识“丑”及其分类

本文对“丑”的内涵及其分类加以论述。     

比喻本体与喻体的关系——以老舍《骆驼祥子》为研究个案

比喻分为显性联系和隐性联系两大类.在其内部,各种比喻的喻体对本体的关系不尽相同:在显性联系比喻中,相似型有陈述、解释、修饰三种,相比型和相等型分别是比较、相等关系;在隐性联系比喻中,同现型有并列、承接、解释、修饰等几种,独现型则是替代关系.各种比喻的喻体与本体间关系的疏密也不一致,由密至疏排列为:独现型-同现型-相等型-相比型和相似型.     

Modeling multiple‐response categorical data from complex surveys

Although “choose all that apply” questions are common in modern surveys, methods for analyzing associations among responses to such questions have only recently been developed. These methods are generally valid only for simple random sampling, but these types of questions often appear in surveys conducted under more complex sampling plans. The purpose of this article is to provide statistical analysis methods that can be applied to “choose all that apply” questions in complex survey sampling situations. Loglinear models are developed to incorporate the multiple responses inherent in these types of questions. Statistics to compare models and to measure association are proposed and their asymptotic distributions are derived. Monte Carlo simulations show that tests based on adjusted Pearson statistics generally hold their correct size when comparing models. These simulations also show that confidence intervals for odds ratios estimated from loglinear models have good coverage properties, while being shorter than those constructed using empirical estimates. Furthermore, the methods are shown to be applicable to more general problems of modeling associations between elements of two or more binary vectors. The proposed analysis methods are applied to data from the National Health and Nutrition Examination Survey. The Canadian Journal of Statistics © 2009 Statistical Society of Canada     

An empirical analysis of the welfare magnet debate using the NLSY

This paper examines the extent to which differences in welfare generosity across states leads to interstate migration. Using microdata from the National Longitudinal Survey of Youth (NLSY) between 1979 and 1992, we employ a quasi-experimental design that utilizes the categorical eligibility of the welfare system. The pattern of cross-state moves among poor single women with children, who are likely to be eligible for benefits is compared to the pattern among other poor households. We find little evidence indicating that welfare-induced migration is a widespread phenomenon. Received: 3 April 1997/Accepted: 4 September 1998     

Immigration and the public sector: Income effects for the native population in Sweden

The immigrants' age structure and labour market situation are major determinants for their net contribution to the public sector. During the 50s, 60s and the 70s the immigrants' net contributions gave positive income effects for the native Swedes. Nowadays there are negative income effects due to the deteriorating employment situation among the immigrants. The yearly positive or negative income effects have at most been 1–2% of the gross national product. A change in the immigrants' employment rate by 1 percentage unit will change their yearly net contribution to the public sector by 0.1% of the gross national product. Received: 2 February 1996/Accepted: 28 July 1998     

高观点下的初等几何之共线问题

根据克莱因变换群理论,欧氏几何是射影几何的子几何.射影几何学的思想理论对欧氏几何具有一定的指导意义,本文仅从几个射影理论就初等几何中的点共线问题的证明进行研究.     

RESE系统可持续发展的分类与转型研究

探索一条“能动地调控自然——社会——经济复合系统,使人类在不超过资源与环境承载能力的条件下,促进经济的发展,保持资源永续和提高生活质量”的持续发展道路,已成为全人类的迫切要求。中外众多学者从不同的角度对实现可持续发展进行了多方面的探索和研完,作者对可持续发展进行了类型划分,提出了系统分类方法,并在此基础上分析了持续发展类型转化作用机制,提出了转型的调控措施,并以淮河流域实例进行了分析。     

Spatial discrimination and classification maps

A method of constructing maps through spatial discrimination is given. The discrimination depends basically on the assumption of local spatial continuity, and a factorized covariance matrix. Given an autocovariance function, this formulation in particular, leads to a deeper insight into the pioneering work of Switzer (1980). Certain windows for the maps are examined, and choice of window size is discussed in relation to the classification error when the variables are dependent versus independent. When a training data is given, we give a method of estimating the parameters in the model. Some numerical examples are also given.     

Robust mixture model cluster analysis using adaptive kernels

The traditional mixture model assumes that a dataset is composed of several populations of Gaussian distributions. In real life, however, data often do not fit the restrictions of normality very well. It is likely that data from a single population exhibiting either asymmetrical or heavy-tail behavior could be erroneously modeled as two populations, resulting in suboptimal decisions. To avoid these pitfalls, we generalize the mixture model using adaptive kernel density estimators. Because kernel density estimators enforce no functional form, we can adapt to non-normal asymmetric, kurtotic, and tail characteristics in each population independently. This, in effect, robustifies mixture modeling. We adapt two computational algorithms, genetic algorithm with regularized Mahalanobis distance and genetic expectation maximization algorithm, to optimize the kernel mixture model (KMM) and use results from robust estimation theory in order to data-adaptively regularize both. Finally, we likewise extend the information criterion ICOMP to score the KMM. We use these tools to simultaneously select the best mixture model and classify all observations without making any subjective decisions. The performance of the KMM is demonstrated on two medical datasets; in both cases, we recover the clinically determined group structure and substantially improve patient classification rates over the Gaussian mixture model.