D- and V-optimal population designs for the quadratic regression model with a random intercept term

In this paper D- and V-optimal population designs for the quadratic regression model with a random intercept term and with values of the explanatory variable taken from a set of equally spaced, non-repeated time points are considered. D-optimal population designs based on single-point individual designs were readily found but the derivation of explicit expressions for designs based on two-point individual designs was not straightforward and was complicated by the fact that the designs now depend on ratio of the variance components. Further algebraic results pertaining to d-point D-optimal population designs where d≥3 and to V-optimal population designs proved elusive. The requisite designs can be calculated by careful programming and this is illustrated by means of a simple example.     

Optimization Model and Algorithm for Crew Management During Airline Irregular Operations

Airline irregular operations have long been a realm where human experience and judgement are the most important tools to utilize. Crew management during irregular operations is usually the bottleneck of the whole system-recovering process due to complicated crew schedules and restrictive crew legalities as well as the size and scope of the hub-and-spoke networks adopted by major carriers. A system-wide multi-commodity integer network flow model and a heuristic search algorithm for the above purpose are presented and discussed in this paper. The computational experiences show that the algorithm is efficient enough to solve problems of realistic size and also has the flexibility to accommodate practical business requirements.     

万能寿险保单盈利能力分析

与传统寿险相比,万能寿险保单具有保障和投资功能、提供最低收益保证、保险利益直接与投资收益相连、保单变更灵活和产品透明等特点,进而万能保单的盈利模式也与传统寿险不同。本文对万能寿险保单的个人账户投资资产构建对数正态分布模型,计算账户价值,利用现金流法计算万能寿险保单公司账户责任准备金,进而计算公司利润水平指标,然后通过随机模拟对影响万能寿险保单盈利能力的个人账户收益率、留存收益率、公司账户投资收益率、贴现利率、生存和死亡概率等因素进行了分析。     

An individual measure of relative survival

Summary.  Relative survival techniques are used to compare survival experience in a study cohort with that expected if background population rates apply. The techniques are especially useful when cause-specific death information is not accurate or not available as they provide a measure of excess mortality in a group of patients with a certain disease. Whereas these methods are based on group comparisons, we present here a transformation approach which instead gives for each individual an outcome measure relative to the appropriate background population. The new outcome measure is easily interpreted and can be analysed by using standard survival analysis techniques. It provides additional information on relative survival and gives new options in regression analysis. For example, one can estimate the proportion of patients who survived longer than a given percentile of the respective general population or compare survival experience of individuals while accounting for the population differences. The regression models for the new outcome measure are different from existing models, thus providing new possibilities in analysing relative survival data. One distinctive feature of our approach is that we adjust for expected survival before modelling. The paper is motivated by a study into the survival of patients after acute myocardial infarction.     

总体分高低成本下的复合多目标抽样方案的设计与应用

文章依据复合多目标抽样的基本原理,提出了高低成本指标广义相关下的多目标复合抽样方法的设计方案,探讨了依广义相关性进行分类的抽样方案在精度和成本方面的优良性,并进行了实际比较与应用。     

含零总体中位数的经验似然

当所研究的总体中含有一定比率的零值,而其余的值大于零时,则称为含零总体。文章利用Owen的经验似然方法来构造这类总体的中位数的置信区间,所得结果不需要假设总体服从某种参数模型,并能利用样本中零值的信息,而且构造的置信区间受非零总体偏斜的影响比一些其他非参数方法小一些。同时,随机模拟的结果也显示了这一点。     

A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces

Differential Evolution (DE) is a simple genetic algorithm for numerical optimization in real parameter spaces. In a statistical context one would not just want the optimum but also its uncertainty. The uncertainty distribution can be obtained by a Bayesian analysis (after specifying prior and likelihood) using Markov Chain Monte Carlo (MCMC) simulation. This paper integrates the essential ideas of DE and MCMC, resulting in Differential Evolution Markov Chain (DE-MC). DE-MC is a population MCMC algorithm, in which multiple chains are run in parallel. DE-MC solves an important problem in MCMC, namely that of choosing an appropriate scale and orientation for the jumping distribution. In DE-MC the jumps are simply a fixed multiple of the differences of two random parameter vectors that are currently in the population. The selection process of DE-MC works via the usual Metropolis ratio which defines the probability with which a proposal is accepted. In tests with known uncertainty distributions, the efficiency of DE-MC with respect to random walk Metropolis with optimal multivariate Normal jumps ranged from 68% for small population sizes to 100% for large population sizes and even to 500% for the 97.5% point of a variable from a 50-dimensional Student distribution. Two Bayesian examples illustrate the potential of DE-MC in practice. DE-MC is shown to facilitate multidimensional updates in a multi-chain “Metropolis-within-Gibbs” sampling approach. The advantage of DE-MC over conventional MCMC are simplicity, speed of calculation and convergence, even for nearly collinear parameters and multimodal densities.     

经济全球化影响下的中国计划生育发展趋势

中国的计划生育是在计划经济体制下形成的,其特点多数与经济全球化的规则有着较大的差距。因此,经济全球化和中国加入世贸组织对中国计划生育的影响是全面而深刻的。在经济全球化的影响下,中国计划生育的发展趋势主要体现在六个方面。     

放开生育政策,促进人口长期均衡发展

通过描述我国人口低生育水平低惯性增长以及人口变化趋势,讨论了人口结构严重失衡对我国经济社会国防国力的消极影响。同时指出,新形势下,完善人口政策必须从改变思想观念开始;必须从放开生育政策,废除现行计划生育政策开始。唯有这样才能实现人口长期均衡发展的目标。     

知识生态学视域下的国家创新系统

国家创新系统具有知识生态特征,其中存在着复杂的知识个体、知识链和知识网络,这些知识节点和网络构成了自身以及创新系统的知识环境。在知识环境中,各知识主体之间的知识流动发挥着不同的作用,它们之间的互动效率成为影响国家创新系统效率的因素,可以根据创新主体之间的知识流动分析国家创新系统的效率。