Innovations to Make Markets More Inclusive for the Poor

Market failures, government failures and some of the characteristics of both the poor and business actors as well as their environment can act as barriers preventing the poor from participating more actively in markets, both as consumers and as producers. Private actors ‐ including for‐profit and not‐for‐profit entities, often in partnership with the public sector ‐ have been able to mitigate some of these constraints through innovations that have helped to make markets more inclusive for the poor, enabling them not just to gain access, but also to participate in ways that enhance their economic empowerment and human development. This article identifies the strategies and innovations used and devises a possible typology for them.     

炎黄时空初探

炎黄称谓最初可能为氏族中杰出的族长人物名字,进而成为部族集群之血缘和精神的宗主神,最终发展成为部族集群的称谓.炎黄早期诞生、发展、迁徒的空间,是今之渭水与泾水流域,这是可以得到文献与考古资料互证的.新石器时代中期,也就是距今8000多年至距今5000多年的"农业革命"时代应为炎黄时代.     

Computational methods for complex stochastic systems: a review of some alternatives to MCMC

We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data.     

基于自动机模型数据关联性能评估算法

提出了一种基于自动机理论的性能评估方法,通过在有限自动机模型中增加描述观测的时间序列,来扩展其对数据关联过程的表达能力;使用时序有限自动机模型来描述关联处理算法的模型;使用带时间属性的字符串表示目标航迹。对数据关联的时序有限自动机模型进行一致性和可达性分析,并在此基础上给出了性能评估算法。实验结果证明该方法可以有效地评价关联算法的性能。     

小波变换思想及其在信号处理中的应用

从傅里叶变换、短时傅里叶变换到小波变换的思想发展过程中分析了小波变换发展的必然性和重要性,并通过MATLAB仿真工具比较了三种变换在信号处理中的应用。实验结果表明,小波变换无论是对信号的时间定位还是对信号的频率定位都优于傅里叶变换和短时傅里叶变换。     

基于时间序列分析方法的连续性抽样调查研究

针对连续性抽样调查中如何利用过去各期的调查信息来提高现期抽样估计精度的问题，引入时间序列分析方法，分别考虑连续性抽样调查中重复样本和重叠样本等不同情况，建立了不同情况下的时间序列模型，利用成熟的时间序列分析方法给出了总体特征的线性组合估计量。由于时间序列分析方法能够充分利用以往各期的调查信息，从而能够给出精度更高的估计量。     

中国收入分配不平等对经济增长影响的研究

文章利用中国30个省、直辖市、自治区1981-2005年的面板数据，运用面板数据模型分析中国收入分配不平等对经济增长的影响，结果表明中国短期内城乡收入差距有利于经济增长。     

后工业时代中的媒介与时间

时间是人乃至人类最内在的存在结构与生成依据.在未竞的现代性的进程中,媒介不断影响和塑造着人的时间意识与观念.媒介化时间的涌现与时间化媒介的膨胀,构成了后工业时代媒介与时间的复杂关系.媒介化时间带给人们以便利,也带来了新的遮蔽与异化.对这一挑战的应答方式之一是进行有效的媒介素养与认知教育.     

Estimation of parameters in incomplete data models defined by dynamical systems

Parametric incomplete data models defined by ordinary differential equations (ODEs) are widely used in biostatistics to describe biological processes accurately. Their parameters are estimated on approximate models, whose regression functions are evaluated by a numerical integration method. Accurate and efficient estimations of these parameters are critical issues. This paper proposes parameter estimation methods involving either a stochastic approximation EM algorithm (SAEM) in the maximum likelihood estimation, or a Gibbs sampler in the Bayesian approach. Both algorithms involve the simulation of non-observed data with conditional distributions using Hastings–Metropolis (H–M) algorithms. A modified H–M algorithm, including an original local linearization scheme to solve the ODEs, is proposed to reduce the computational time significantly. The convergence on the approximate model of all these algorithms is proved. The errors induced by the numerical solving method on the conditional distribution, the likelihood and the posterior distribution are bounded. The Bayesian and maximum likelihood estimation methods are illustrated on a simulated pharmacokinetic nonlinear mixed-effects model defined by an ODE. Simulation results illustrate the ability of these algorithms to provide accurate estimates.