企业集群风险因素评价体系的研究

企业集群作为一种中间组织形态,正深刻影响着区域经济的绩效,不论是在发达国家,如美国硅谷、意大利的普拉托毛纺集群,还是发展中国家,如温州的打火机集群、中关村现象、东莞计算机设备集群,成功集群的案例不断涌现.但是,促进集群形成与发展的因素并不能保证企业集群的长期繁荣,即使企业集群发展得比较成熟的欧美国家,失败的企业集群也不胜枚举.     

区域品牌的治理机制研究:基于产业集群的视角

基于产业集群视角的区域品牌涵盖了区域经济、产业集群以及品牌营销等综合经济活动,是产业集群发展到高级阶段的重要表征之一.然而,由于区域品牌在一定程度上具有准公共品的属性,由此潜藏了机会主义行为风险,并同时产生了治理的必要性.文章首先界定了区域品牌的内涵、以及其与产业集群的互动机理;然后对区域品牌进行关于准公共品属性以及风险的经济学分析;最后针对区域品牌中的治理问题,以基于产业集群的视角进行系统阐述,并构建基于四个关键运行机制的区域品牌治理概念模型,以期为我国区域品牌的进一步研究与实践提供参考.     

产业集群协同演化与策略选择

产业集群协同演化是指产业集群内以各企业等要素间的相互作用为基础的经济联合体,协同竞争是产业集群演化的重要动力。本文基于Lotka-Volterra模型分析,对产业集群内企业之间的捕食与竞争关系进行研究,认为产业集群协同演化策略选择需要重视产业集群演化的协同效应,防止过度竞争;应以正确的企业发展战略为导向;以产业集群核心竞争力的培养和优化为依归;注重产业集群内企业之间的竞合策略等。     

产业集群与区域经济耦合效应的评价

研究认为,产业集群与区域经济耦合效应,不仅表现在两个子系统之间的耦合协调程度,而且还表现在这种协调作用下的系统耦合效率。文章在分析产业集群与区域经济交互耦合关系的基础上,采用耦合协调度模型与DEA模型,对产业集群与区域经济耦合效应进行评价。     

基于社会网络分析的产业集群定量化模型

近几年产业集群对地区经济发展影响显著,吸引了许多学者加入到产业集群研究中,这些学者涉及许多学科领域,其中包括经济地理学、产业组织学、社会经济学、技术经济学等,新经济社会学的出现,为产业集群研究开辟了新的视角,本文尝试从新经济社会学的视角,特别运用与之相联系的社会网络分析理论与方法进行产业集群定量化研究,旨在为产业集群定量化理论奠定一定的基础.     

集群的经济学分析

产业集群是现代经济发展中的一个重要现象,在西方国家,随着市场经济的逐渐发展与完善,一些经济管理学家从各个角度对集群形成这一重要的经济组织现象进行了一些研究.在我国,以专业产品区、专业镇为主的产业集群网络形成了民营企业的群体力量.集群地区内不断集聚的资源、信息和企业配套能力使企业间相互沟通、要素供给与技术扩散更加方便,交易成本降低、资源配置效率提高,与外部市场、信息、资源利企业(包括银行等金融机构)之间的扩大交换更具竞争力.     

湖北省产业集群的鉴别及其特征

0引言产业集群理论强调产业的空间集聚有利于经济增长和地方发展,引发了公共部门及政策制定者的兴趣,并发展出以集群概念为主轴的政策分析模式,包括以集群为核心的产业经济政策,以集群分析为基础的经济发展战略,及集群的推广与执行策略等。但在政策制定过程中,产业集群的界定通     

中原城市群、产业集群耦合与区域经济增长

城市群、产业集群及其耦合关系会对区域经济发展产生深刻影响。文章以中原城市群为研究地理单元,城市集聚、产业集群、城市群产业集群耦合集聚、全球经济联系为解释变量,以区域经济增长为被解释变量,以耦合协调度衡量城市群与产业集群耦合互动水平,通过单位根检验、协整检验构建计量经济学回归模型做进一步的实证研究。结果表明,城市集聚对区域经济发展有促进作用,产业集群对区域经济增长有显著性影响,产业集群对区域经济增长的贡献度大于城市群。在城市群与产业集群系统耦合协调状态下,城市群和产业集群耦合互动对区域经济增长有显著性影响。外商直接投资对中原城市群经济发展贡献并不明显,跨国公司和外商投资力度还很欠缺,中原城市群需要持续扩大外商投资规模和提升外商投资质量。     

现代软件及网络技术对统计工作推动的探索——《统计数据急装箱》在统计工作中的应用

统计数据急装箱《统计数据急装箱》软件平台推广以来,通过在四川省企调队以及地方统计局的成功应用,我们对统计信息化工作作了一些探索,以下是一些探索经验。目前,在统计系统内部拥有纵横交错的高速网络,而对它的利用却不够,无非是报表、文件传送,上网,收发电子邮件等。我们可以利用这样的高速网络来建立集群的、高效的网络数据处理系统,提高统计报表处理的效率。就统计系统而言,实用的网络数据处理系统应具备以下几个特点:一是模式精简,易于推广;_二是操作简单,易于学习;三是可以有不同的功能组合,以满足不同统计部门需要。我们可以考虑从…     

战略性新兴产业集群与区域经济空间耦合发展效率测度方法研究

为定量测度中国战略性新兴产业集群与各级区域经济空间的耦合发展效率,以容量耦合的分析框架为基础,构建战略性新兴产业集群与区域经济空间耦合系统及其耦合发展效率测度模型。以2010年度横截面数据为样本,对中国31个省份战略性新兴产业集群与区域经济空间的耦合发展效率进行实际测度,结果表明:中国东部地区、东北地区、中部地区以及西部地区的战略性新兴产业集群与区域经济空间的耦合发展效率依次降低;31个省份中,上海、江苏、浙江、山东、广东、安徽6省(市)战略性新兴产业集群领先于区域经济空间的发展效率,其余25个省(市)的发展效率则滞后于区域经济空间。     

Maximum-likelihood estimation of the random-clumped multinomial model as a prototype problem for large-scale statistical computing

Numerical methods are needed to obtain maximum-likelihood estimates (MLEs) in many problems. Computation time can be an issue for some likelihoods even with modern computing power. We consider one such problem where the assumed model is a random-clumped multinomial distribution. We compute MLEs for this model in parallel using the Toolkit for Advanced Optimization software library. The computations are performed on a distributed-memory cluster with low latency interconnect. We demonstrate that for larger problems, scaling the number of processes improves wall clock time significantly. An illustrative example shows how parallel MLE computation can be useful in a large data analysis. Our experience with a direct numerical approach indicates that more substantial gains may be obtained by making use of the specific structure of the random-clumped model.     

Massively parallel computing: a statistical application

Massively parallel computing is a computing environment with thousands of subprocessors. It requires some special programming methods, but is well suited to certain imaging problems. One such statistical example is discussed in this paper. In addition there are other natural statistical problems for which this technology is well suited. This paper describes our experience, as statisticians, with a massively parallel computer in a problem of image correlation spectroscopy. Even with this computing environment some direct computations would still take in the order of a year to finish. It is shown that some of the algorithms of interest can be made parallel.     

Parallel multivariate slice sampling

Slice sampling provides an easily implemented method for constructing a Markov chain Monte Carlo (MCMC) algorithm. However, slice sampling has two major drawbacks: (i) it requires repeated evaluation of likelihoods for each update, which can make it impractical when evaluations are expensive or as the number of evaluations grows (geometrically) with the dimension of the slice sampler, and (ii) since it can be challenging to construct multivariate updates, the updates are typically univariate, which often results in slow mixing samplers. We propose an approach to multivariate slice sampling that naturally lends itself to a parallel implementation. Our approach takes advantage of recent advances in computer architectures, for instance, the newest generation of graphics cards can execute roughly 30,000 threads simultaneously. We demonstrate that it is possible to construct a multivariate slice sampler that has good mixing properties and is efficient in terms of computing time. The contributions of this article are therefore twofold. We study approaches for constructing a multivariate slice sampler, and we show how parallel computing can be useful for making MCMC algorithms computationally efficient. We study various implementations of our algorithm in the context of real and simulated data.     

Reliability Analysis of Flexible Manufacturing Cells Based on Triangular Fuzzy Number

In most of flexible production schemes, the flexible manufacturing cells (FMCs) are more economical and feasible. So, an important task is to establish the correct reliability analysis model for the FMCs. However, with the increasing of system complexity, some reliability analysis modes can hardly describe the actual situation. Besides, due to the lack of test-data and field-data during the design stage of FMC' system, the reliability modeling will be more complicated. In order to deal with the deficient data and the uncertainty occurred from analysis and judgment, this article analyzes the reliability of FMCs system through the method of fuzzy fault tree, which is based on triangular fuzzy membership. At last, a practical example is illustrated. The reliability analysis model indicates that it can offer a diagnostic tool for FMCs system and improve the efficiency of operation and production in FMCs system.     

Fast,closed-form,and efficient estimators for hierarchical models with AR(1) covariance and unequal cluster sizes

This article is concerned with statistically and computationally efficient estimation in a hierarchical data setting with unequal cluster sizes and an AR(1) covariance structure. Maximum likelihood estimation for AR(1) requires numerical iteration when cluster sizes are unequal. A near optimal non-iterative procedure is proposed. Pseudo-likelihood and split-sample methods are used, resulting in computing weights to combine cluster size specific parameter estimates. Results show that the method is statistically nearly as efficient as maximum likelihood, but shows great savings in computation time.     

A Study of Student Reaction to the Use of Minitab in an Introductory Statistics Course

Traditionally, the introductory statistics course, Principles of Statistics (STAT 101), at Iowa State University has been taught without reference to a statistical analysis computing package. Although important for the implementation of statistical techniques, a computer component has been perceived by instructors to take time away from the coverage of statistical topics. To gauge students' reactions to the usefulness of a statistical computing package, an experiment was conducted during the fall term of 1986. Volunteers from a STAT 101 class were randomly assigned to either a control group or a computer use group. Both groups filled out questionnaires at the beginning and end of the semester. During the semester, the computer use group had access to and instruction in the use of Minitab. This instruction was tied to homework and laboratory assignments for the course. This article presents results of this experiment. On the basis of the responses to the questionnaires, the value of a statistical computing package as a pedagogical tool is examined. Recommendations for the use of a statistical computing package in a large introductory statistics course are made.     

Is categorization of random data necessary for parallel analysis on Likert-type data?

Random eigenvalues are the key elements in parallel analysis. When analyzing Likert-type data, is it necessary to convert the continuous random data to discrete type before estimating eigenvalues? The study compared the random eigenvalues obtained from continuous and categorized random data from two popular computer programs to be used as the basis for comparison in conducting parallel analysis on Likert-type data. Results indicated that categorized random data gave eigenvalues and number of factors similar to those obtained from continuous random data. It is suggested that when conducting parallel analysis on Likert-type data by the two programs, the conversion is unnecessary.     

Parallel Markov chain Monte Carlo for Bayesian hierarchical models with big data,in two stages

Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common for the full data set, with most parameters being group specific. Thus, parallel Bayesian MCMC methods that take into account the structure of the model and split the full data set by groups rather than by observations are a more natural approach for analysis. Here, we adapt and extend a recently introduced two-stage Bayesian hierarchical modeling approach, and we partition complete data sets by groups. In stage 1, the group-specific parameters are estimated independently in parallel. The stage 1 posteriors are used as proposal distributions in stage 2, where the target distribution is the full model. Using three-level and four-level models, we show in both simulation and real data studies that results of our method agree closely with the full data analysis, with greatly increased MCMC efficiency and greatly reduced computation times. The advantages of our method versus existing parallel MCMC computing methods are also described.     

Parallelizing computation of expected values in recombinant binomial trees

Recombinant binomial trees are binary trees where each non-leaf node has two child nodes, but adjacent parents share a common child node. Such trees arise in option pricing in finance. For example, an option can be valued by evaluating the expected payoffs with respect to random paths in the tree. The cost to exactly compute expected values over random paths grows exponentially in the depth of the tree, rendering a serial computation of one branch at a time impractical. We propose a parallelization method that transforms the calculation of the expected value into an embarrassingly parallel problem by mapping the branches of the binomial tree to the processes in a multiprocessor computing environment. We also discuss a parallel Monte Carlo method and verify the convergence and the variance reduction behavior by simulation study. Performance results from R and Julia implementations are compared on a distributed computing cluster.     

On Signatures of Series and Parallel Systems Consisting of Modules with Arbitrary Structures

The signature of a system is a useful concept not only in the analysis of binary coherent systems but also in network reliability. Computation of system signature is a well-defined combinatorial problem. This article is concerned with the computation of signature vectors of series and parallel systems consisting of modules. We derive simple formulas for the signature and minimal signature of series and parallel systems based on signatures and minimal signatures of modules with given structures. We present computational results to illustrate the findings.