写作过程与大学生英语写作能力训练

大学生英语写作能力的提高在于教师得当的教学方法和学生的大量的写作实践,得当的教学方法会激发学生写作的积极性和创造性,同时可以减轻教师批阅压力,使师生双方受益。这种方法就是教师充分利用写作过程中的预写阶段、写作阶段和修改阶段来全面训练和培养学生的写作能力。     

英文段落的推展模式及内部衔接机制初探

从篇章语言学的角度看 ,英文段落依层级的概括性推展 ,并呈典型的直线性发展模式。英文作者主要利用词汇相等链、链式结构和编列结构衔接段中各语句来推展段落 ,从而使段落具有交际功能。英文语篇结构模式对英文习得者来说具有实际指导意义     

计算机公共课教学改革实践探索

在科学技术高度发达的今天 ,为了使计算机公共课教学适应时代的发展 ,教师必须转变教育观念。同时 ,针对计算机教学自身的特点 ,教师应当运用变化的教学手段和灵活多样的教学方法来进行教学     

试论企业的环境成本管理

近年来随着人们环保意识的增强,企业对环境成本的管理日趋重视。本文从环境成本的内涵入手,阐述了环境成本的管理指导思想,并提出了环境成本管理的方法。     

世界教育信息化发展及其经验

教育信息化是战后教育发展的最重要发展方向之一。世界教育信息化的发展经历了发端、进一步推进和迅速高涨的发展时期;在每一个阶段各国推进教育信息化的政策、措施和特点等方面都呈现出一些独自的特征。总结世界各国推进教育信息化的经验对我国教育信息化的发展具有重要的启发意义。     

ELICITING A DIRECTED ACYCLIC GRAPH FOR A MULTIVARIATE TIME SERIES OF VEHICLE COUNTS IN A TRAFFIC NETWORK

The problem of modelling multivariate time series of vehicle counts in traffic networks is considered. It is proposed to use a model called the linear multiregression dynamic model (LMDM). The LMDM is a multivariate Bayesian dynamic model which uses any conditional independence and causal structure across the time series to break down the complex multivariate model into simpler univariate dynamic linear models. The conditional independence and causal structure in the time series can be represented by a directed acyclic graph (DAG). The DAG not only gives a useful pictorial representation of the multivariate structure, but it is also used to build the LMDM. Therefore, eliciting a DAG which gives a realistic representation of the series is a crucial part of the modelling process. A DAG is elicited for the multivariate time series of hourly vehicle counts at the junction of three major roads in the UK. A flow diagram is introduced to give a pictorial representation of the possible vehicle routes through the network. It is shown how this flow diagram, together with a map of the network, can suggest a DAG for the time series suitable for use with an LMDM.     

经济预测中的Monte Carlo模拟及方差控制——以国际石油价格预测为例

从MDnte Carlo模拟的实现过程入手，首先通过对Monte Carlo方法原理的阐述来介绍该种方法。进一步结合具体的实例通过计算机进行模拟来解释Monte Carlo方法的具体实现过程。重点讨论在选择合理的数据生成过程的前提下，如何在Monte Carlo方法中减少模拟方差，从而提高估计精度，更好地应用这种方法来进行经济预测。     

A Sparse Implementation of the Average Information Algorithm for Factor Analytic and Reduced Rank Variance Models

Factor analytic variance models have been widely considered for the analysis of multivariate data particularly in the psychometrics area. Recently Smith, Cullis & Thompson (2001) have considered their use in the analysis of multi‐environment data arising from plant improvement programs. For these data, the size of the problem and the complexity of the variance models chosen to account for spatial heterogeneity within trials implies that standard algorithms for fitting factor analytic models can be computationally expensive. This paper presents a sparse implementation of the average information algorithm (Gilmour, Thompson & Cullis, 1995) for fitting factor analytic and reduced rank variance models.     

Some results on optimality in models with heteroscedastic errors from partial optimum designs

This paper studies optimum designs for linear models when the errors are heteroscedastic. Sufficient conditions are given in order to obtainD-, A- andE-optimum designs for a complete regression model from partial optimum designs for some sub-parameters. A result about optimality for a complete model from the optimality for the submodels is included. Supported by Junta de Andalucía, research group FQM244.     

Block external bootstrap in partially linear models with nonstationary strong mixing error terms

The authors extend the block external bootstrap to partially linear regression models with strongly mixing, nonstationary error terms. In addition to providing an approximate distribution for the semiparametric least square estimator of the parametric component, they propose a consistent estimator of the co‐variance matrix of this estimator.