均值绝对偏差资产组合选择模型的算法

对均值绝对偏差模型进行了简化,并利用一种旋转算法求解。这种算法比单纯形算法的计算简便,且计算量更小。利用上海和深圳股市1 072支股票70期周末收盘价所作的实验结果表明,对于资产无上界限制的模型,计算20个不同最优投资组合需要1 274次旋转运算,上界为10%时需要1 570次旋转运算,每 次旋转运算约需1 14171次加法和乘法运算。     

房地产企业留住人才的博弈分析

本文通过建立博弈矩阵模型,并利用线性规划理论进行分析,指出房地产企业在提高工资待遇吸引人才的同时,建立有效的动态激励机制才是留住人才的关键.     

对偶线性规划问题性质探析

探讨对偶线性规划的原始问题与对偶问题的属性，阐述两者的区别和内在联系，用较简便的方法论证其重要性质，揭示可行解与目标函数、可行解与最优解的关系，指出线性规划问题最优解从约束条件较少的对偶问题寻求为另一较简便之方法。     

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     

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.     

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.     

Human Salmonellosis: Estimation of Dose-Illness from Outbreak Data

The quantification of the relationship between the amount of microbial organisms ingested and a specific outcome such as infection, illness, or mortality is a key aspect of quantitative risk assessment. A main problem in determining such dose-response models is the availability of appropriate data. Human feeding trials have been criticized because only young healthy volunteers are selected to participate and low doses, as often occurring in real life, are typically not considered. Epidemiological outbreak data are considered to be more valuable, but are more subject to data uncertainty. In this article, we model the dose-illness relationship based on data of 20 Salmonella outbreaks, as discussed by the World Health Organization. In particular, we model the dose-illness relationship using generalized linear mixed models and fractional polynomials of dose. The fractional polynomial models are modified to satisfy the properties of different types of dose-illness models as proposed by Teunis et al . Within these models, differences in host susceptibility (susceptible versus normal population) are modeled as fixed effects whereas differences in serovar type and food matrix are modeled as random effects. In addition, two bootstrap procedures are presented. A first procedure accounts for stochastic variability whereas a second procedure accounts for both stochastic variability and data uncertainty. The analyses indicate that the susceptible population has a higher probability of illness at low dose levels when the combination pathogen-food matrix is extremely virulent and at high dose levels when the combination is less virulent. Furthermore, the analyses suggest that immunity exists in the normal population but not in the susceptible population.     

线性科学与非线性科学哲学思想研究

在简述线性科学的科学基础、科学方法和哲学思想的基础上，研究了非线性科学的科学基础、科学方法和哲学思想，其中较为详细地探讨了非线性科学的哲学范畴和哲学思想，认为要多角度观察事物，要从变化中认识主要矛盾，事物的发展是多元竞争与协同的统一，这一结果将对非线性科学的哲学思想的进一步研究有一定的启发和借鉴作用。