和谐社会构建中的利益表达与利益协调

我国正处在一个社会转型的时代,利益关系格局也发生了一定的变化,产生了利益分化和重组。在这个利益分化的时代,中国的经济利益关系格局出现了经济利益主体多元化、经济利益来源多样化、经济利益差距扩大化和经济利益关系复杂化等新的特征。对此,我们必须正视问题,应特别注意利益表达机制的构建并进行合理的利益协调,充分发挥市场、政府和利益集团的作用,只有这样才能构建一个和谐的社会。     

Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t-distribution

Summary . A fairly general procedure is studied to perturb a multivariate density satisfying a weak form of multivariate symmetry, and to generate a whole set of non-symmetric densities. The approach is sufficiently general to encompass some recent proposals in the literature, variously related to the skew normal distribution. The special case of skew elliptical densities is examined in detail, establishing connections with existing similar work. The final part of the paper specializes further to a form of multivariate skew t -density. Likelihood inference for this distribution is examined, and it is illustrated with numerical examples.     

英语学习中的语言迁移现象分析

以英语学习中出现的正向迁移和负向迁移为例证,分析汉语对学习英语所产生的影响,而重点论述以汉语为母语的思维定势对英语学习产生的负向迁移影响,并提出了如何排除母语干扰的若干意见。     

英语否定词在语言表达中的应用

否定词的用法对学习英语的学生来说一直是个难题。因为在英语中否定词的用法和意义是灵活多变的。本文主要从四个方面阐述了英语否定词在语言表达中的具体应用。旨在帮助学生更有效 ,灵活地理解和使用。     

Estimation of noncentrality parameters

We propose some estimators of noncentrality parameters which improve upon usual unbiased estimators under quadratic loss. The distributions we consider are the noncentral chi-square and the noncentral F. However, we give more general results for the family of elliptically contoured distributions and propose a robust dominating estimator.     

文学思潮中的“花花公子”——简论“欲望化写作”

欲望化写作是当今文坛上方兴未艾的且引人注目的文学现象,其注重张扬感官放纵和感官感受。本文拟从欲望化写作的成因、分类以及欲望化写作的积极影响和消极影响等几个方面来探讨欲望化思潮。     

大学生的心态状况调查及其积极心态培养对策研究

文章利用一般自我效能感量表和简易应对方式量表,对某校大学生作抽样调查,结果显示:不同性别和专业的大学生在自我效能感和应对方式上无显著差异;大学生自我效能感的来源存在偏差,主导性的应对方式也有错位。这表明当代大学生的心态并非都是积极的。因此,高校思想政治工作者要高度关注和研究大学生的心态问题,要培养大学生基于能力的自我效能感,要形成以行动为主导的应对方式,要建立起积极心态培训机制。     

线性矩阵不等式问题的进化计算解决方法

针对决定模糊控制中稳定性的线性矩阵不等式问题,提出了用进化计算来解决模糊控制中线性矩阵不等式的新算法。实验证明,该算法解“用于实现模糊控制的增益调度和稳定性的线性矩阵不等式”是有效的。     

Generalized additive models for location, scale and shape

Summary.  A general class of statistical models for a univariate response variable is presented which we call the generalized additive model for location, scale and shape (GAMLSS). The model assumes independent observations of the response variable y given the parameters, the explanatory variables and the values of the random effects. The distribution for the response variable in the GAMLSS can be selected from a very general family of distributions including highly skew or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only of the mean (or location) but also of the other parameters of the distribution of y , as parametric and/or additive nonparametric (smooth) functions of explanatory variables and/or random-effects terms. Maximum (penalized) likelihood estimation is used to fit the (non)parametric models. A Newton–Raphson or Fisher scoring algorithm is used to maximize the (penalized) likelihood. The additive terms in the model are fitted by using a backfitting algorithm. Censored data are easily incorporated into the framework. Five data sets from different fields of application are analysed to emphasize the generality of the GAMLSS class of models.     

Bayesian analysis of a marked point process: Application in seismic hazard assessment

Point processes are the stochastic models most suitable for describing physical phenomena that appear at irregularly spaced times, such as the earthquakes. These processes are uniquely characterized by their conditional intensity, that is, by the probability that an event will occur in the infinitesimal interval (t, t+Δt), given the history of the process up tot. The seismic phenomenon displays different behaviours on different time and size scales; in particular, the occurrence of destructive shocks over some centuries in a seismogenic region may be explained by the elastic rebound theory. This theory has inspired the so-called stress release models: their conditional intensity translates the idea that an earthquake produces a sudden decrease in the amount of strain accumulated gradually over time along a fault, and the subsequent event occurs when the stress exceeds the strength of the medium. This study has a double objective: the formulation of these models in the Bayesian framework, and the assignment to each event of a mark, that is its magnitude, modelled through a distribution that depends at timet on the stress level accumulated up to that instant. The resulting parameter space is constrained and dependent on the data, complicating Bayesian computation and analysis. We have resorted to Monte Carlo methods to solve these problems.