沈阳装备制造业循环经济体系的构建

装备制造业始终在沈阳经济发展中起着主导作用,然而其能源消耗大、污染比较严重,因此必须倡导循环经济理念,构建循环经济发展模式,其总体战略可以概括为“3+1”循环经济体系,即实施小、中、大三层循环,并发展一个重要产业——绿色再生产业：在微观企业层面,大力推行清洁生产,建立小循环;在中观区域层面,构建相关生态产业链,形成中循环;在宏观社会层面,构建循环型城市,形成大循环;大力发展废物处置和再生产业,实施回收再制造发展模式。     

辽宁装备制造业产业链升级的路径及对策

产业链升级就是通过高效整合整条产业链上的各个环节,实现资源优化配置、效益最高的目标。在分析辽宁装备制造业产业链现状的基础上,提出辽宁装备制造业产业链升级的目标与要求,建立装备制造业产业链升级测评体系,并通过考察辽宁2005—2007年装备制造业技术升级能力、生产制造能力、产业配套能力和环保能力4方面的数据,运用灰色评估法对辽宁装备制造业产业链升级能力进行测评,根据其升级目标和升级能力,提出辽宁装备制造业产业链升级的路径和对策。     

SV模型下中国股票型开放式基金杠杆效应分析

针对中国股票型开放式基金收益波动中是否存在杠杆效应的问题,在对该类基金整体及所选取的三支具有代表性的单个基金分析的基础上,运用一个带杠杆效应的SV模型对其收益的波动性建模,并利用MCMC方法对模型进行参数估计。结果显示：不同于一般对股票市场的研究结论,无论股票型开放式基金整体还是单个基金,其收益率序列的波动中均不存在显著的杠杆效应。     

一种基于SIS的贝叶斯模型算法

贝叶斯动态模型及其预测理论具有广泛的应用性,如在通信,控制,人工智能,经济管理,气象预报等领域。本文简要介绍了贝叶斯动态模型,对于非线性贝叶斯动态模型提出了SIS算法及其在处理非线性模型中应用。     

在ARIMA(0,1,1)需求下的牛鞭效应与信息共享的评价

本文考虑一个包含一个供应商和一个零售商的两级供应链,研究在需求模型ARIMA(0,1,1)下牛鞭效应的量化和信息共享的价值,比较信息共享之前和之后的差异,其结果表明信息共享能给供应商带来减轻牛鞭效应、减少现有平均库存以及降低成本等好处。     

Bayesian inference for generalized additive mixed models based on Markov random field priors

Most regression problems in practice require flexible semiparametric forms of the predictor for modelling the dependence of responses on covariates. Moreover, it is often necessary to add random effects accounting for overdispersion caused by unobserved heterogeneity or for correlation in longitudinal or spatial data. We present a unified approach for Bayesian inference via Markov chain Monte Carlo simulation in generalized additive and semiparametric mixed models. Different types of covariates, such as the usual covariates with fixed effects, metrical covariates with non-linear effects, unstructured random effects, trend and seasonal components in longitudinal data and spatial covariates, are all treated within the same general framework by assigning appropriate Markov random field priors with different forms and degrees of smoothness. We applied the approach in several case-studies and consulting cases, showing that the methods are also computationally feasible in problems with many covariates and large data sets. In this paper, we choose two typical applications.     

Markov Chains and De-initializing Processes

We define a notion of de-initializing Markov chains. We prove that to analyse convergence of Markov chains to stationarity, it suffices to analyse convergence of a de-initializing chain. Applications are given to Markov chain Monte Carlo algorithms and to convergence diagnostics.     

Bounding convergence rates for Markov chains: An example of the use of computer algebra

Kolassa and Tanner (J. Am. Stat. Assoc. (1994) 89, 697–702) present the Gibbs-Skovgaard algorithm for approximate conditional inference. Kolassa (Ann Statist. (1999), 27, 129–142) gives conditions under which their Markov chain is known to converge. This paper calculates explicity bounds on convergence rates in terms calculable directly from chain transition operators. These results are useful in cases like those considered by Kolassa (1999).     

A BAYESIAN INTERPRETATION OF MULTIPLE POINT ESTIMATES

Consider a large number of econometric investigations using different estimation techniques and/or different subsets of all available data to estimate a fixed set of parameters. The resulting empirical distribution of point estimates can be shown - under suitable conditions - to coincide with a Bayesian posterior measure on the parameter space induced by a minimum information procedure. This Bayesian interpretation makes it easier to combine the results of various empirical exercises for statistical decision making. The collection of estimators may be generated by one investigator to ensure the satisfaction of our conditions, or they may be collected from published works, where behavioral assumptions need to be made regarding the dependence structure of econometric studies.     

人力资源质量成长链及其多级混成研究

基于社会系统工程思想,本文首先提出了人力资源质量成长环和成长链的概念,然后分析了人力资源质量的多级混成过程。