东临碣石在绥中——《我从龙河走来》选载

读初中的时候就背诵过魏武帝曹操东临碣石,以观沧海这句脍炙人口的诗篇.那么,碣石究竟在哪里呢?我们到辽河流域采访时终于找到了答案: 碣石就在辽宁省绥中县.     

浅析李大钊对阶级斗争学说的阐述

李大钊是在十月革命之后在中国第一个认识到马克思列宁主义真理性的革命先驱者,他比较系统的阐述了马克思的唯物史观理论并以讲学和著述的方式加以传播,阶级斗争学说是李大钊对马克思主义学说阐述的重要内容,本文将叙述他对于阶级斗争学说的阐释,并对其相关阐释进行分析。     

中国经济增长与环境污染关系的Kuznets曲线

一、引言关于经济增长与环境污染之间的关系,国内外很多学者进行了大量研究。1992年,美国经济学家G·Grossman和A·Kureger对此提出了一个环境Kuznets曲线(Environment Kuznets Curve,EKC)的假设[1]。该假设试图说明如果没有一定的环境政策干预,一个国家的整体环境质量或污染水平是随着经济增长和经济实力的积累呈先恶化后改善的趋势。他们采用跨国数据说明了EKC的存在,即最初环境恶化程度随着人均GDP的上升而上升,达到一个转折点后,将随着人均GDP的上升而下降。并把这种现象归因于以下几点:(1)当人们越富有时,对环境质量的要求越高…     

成电8010分布式計算机之特点

成电8010是基于单板机开发成功的分布式微机过程控制系统。该系统的性能特点是任务的动态调度、机间合作、优美降级和资源分享等。这些特点使其应用非常成功。本文就是对这些性能特点作相应的分析和介绍。     

Regular A-optimal design matrices X = (xij) with xij = ?1, 0, 1

New construction methods of the regular A-optimal design matrices with elements −1, 0, 1 are presented, under assumption of nonhomogeneity of variance error. The presented constructions are based on the incidence matrices of the balanced bipartite weighing designs.     

Vector random fields with compactly supported covariance matrix functions

The objective of this paper is to construct covariance matrix functions whose entries are compactly supported, and to use them as building blocks to formulate other covariance matrix functions for second-order vector stochastic processes or random fields. In terms of the scale mixture of compactly supported covariance matrix functions, we derive a class of second-order vector stochastic processes on the real line whose direct and cross covariance functions are of Pólya type. Then some second-order vector random fields in R^d${\mathbb{R}}^d$ whose direct and cross covariance functions are compactly supported are constructed by using a convolution approach and a mixture approach.     

Three-part model for fractional response variables with application to Chinese household health insurance coverage

A survey on health insurance was conducted in July and August of 2011 in three major cities in China. In this study, we analyze the household coverage rate, which is an important index of the quality of health insurance. The coverage rate is restricted to the unit interval [0, 1], and it may differ from other rate data in that the “two corners” are nonzero. That is, there are nonzero probabilities of zero and full coverage. Such data may also be encountered in economics, finance, medicine, and many other areas. The existing approaches may not be able to properly accommodate such data. In this study, we develop a three-part model that properly describes fractional response variables with non-ignorable zeros and ones. We investigate estimation and inference under two proportional constraints on the regression parameters. Such constraints may lead to more lucid interpretations and fewer unknown parameters and hence more accurate estimation. A simulation study is conducted to compare the performance of constrained and unconstrained models and show that estimation under constraint can be more efficient. The analysis of household health insurance coverage data suggests that household size, income, expense, and presence of chronic disease are associated with insurance coverage.     

A Bayesian Meta-Modeling Approach for Gaussian Stochastic Process Models Using a Non Informative Prior

In this article, an efficient Bayesian meta-modeling approach is proposed for Gaussian stochastic process models in computer experiments. Different prior densities and particularly, a non informative hyper prior have been employed on the parameters involved in the correlation matrix. And the estimation of related parameters is obtained by the expectation-maximization algorithm. Compared with the recent work of Li and Sudjianto (2005 Li , R. , Sudjianto , A. ( 2005 ). Analysis of computer experiments using penalized likelihood in Kriging models . Technometrics 47 : 111 – 120 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), the proposed approach is not only of higher prediction accuracy but also of lower computational cost, due to the utilization of the non informative prior and the absence of tuning parameters. Experimental results demonstrate that our approach yields state-of-the-art performance.     

Empirical Analysis of Interval-Censored Failure Time Data with Linear Transformation Models

Interval-censored data naturally arise in many studies. For their regression analysis, many approaches have been proposed under various models and for most of them, the inference is carried out based on the asymptotic normality. In particular, Zhang et al. (2005) discussed the procedure under the linear transformation model. It is well-known that the symmetric property implied by the normal distribution may not be appropriate sometimes. Also the method could underestimate the variance of estimated parameters. This paper proposes an empirical likelihood-based procedure for the problem. Simulation and the analysis of a real data set are conducted to assess the performance of the procedure.     

An augmented data scoring algorithm for maximum likelihood

The expectation-maximization (EM) method facilitates computation of max¬imum likelihood (ML) and maximum penalized likelihood (MPL) solutions. The procedure requires specification of unobservabie complete data which augment the measured or incomplete data. This specification defines a conditional expectation of the complete data log-likelihood function which is computed in the E-stcp. The EM algorithm is most effective when maximizing the iunction Q{0) denned in the F-stnp is easier than maximizing the likelihood function.

The Monte Carlo EM (MCEM) algorithm of Wei & Tanner (1990) was introduced for problems where computation of Q is difficult or intractable. However Monte Carlo can he computationally expensive, e.g. in signal processing applications involving large numbers of parameters. We provide another approach: a modification of thc standard EM algorithm avoiding computation of conditional expectations.