一类带有分布参数系统的最佳控制

本文给出一类非均匀弦的横向振动的最佳控制，推广了P C Park的结果。这一结果同样适用于同类型振动问题。     

全球化视野下党的建设探析

全球化使执政的中国共产党的现代化建设面临着新的形势 ,要推动中国社会主义事业的顺利发展 ,必须要从思想、组织和作风等方面不断加强党的建设     

人的全面发展理论的现代开拓及其意义

人的全面发展,作为共产主义追求的理想价值目标,它表现为人的整体素质的辩证和谐统一.未来社会评价一个国家的根本尺度不再只是经济的增长和科技水平的提高,同时也包含着人的因素的进步和发展;现代社会除了物质文明的发达之外,更重要的是对精神文明、制度文明、社会的可持续发展以及国民素质完善的高度关注.     

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.     

论党的十七大的重大理论贡献

党的十七大的理论贡献在于，首次提出“中国特色社会主义理论体系”这一新概念，有力地回答了在改革开放关键时期“举什么旗，走什么路”的根本问题；明确界定了科学发展观的内涵和全面建设小康社会的新要求，提出了生态文明理念，有力地回答了在21世纪头20年“怎么举好旗，怎么走好路”的基本问题；突出强调推动中国特色社会主义民主政治制度和以民生为重点的社会建设，有力地回答了“如何发展社会主义政治制度，如何保障和改善民生”两大难题。     

在中国特色社会主义新起点上推进新的思想解放

解放思想，是党的思想路线的本质要求，是我们应对前进道路上各种新情况新问题、不断开创事业新局面的一大法宝，是中国特色社会主义的思想精髓。要把中国特色社会主义事业不断推向前进，必须在新起点上推进新的思想解放。     

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.     

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.