Improved Monte Carlo inference for models with additive error

Some statistical models defined in terms of a generating stochastic mechanism have intractable distribution theory, which renders parameter estimation difficult. However, a Monte Carlo estimate of the log-likelihood surface for such a model can be obtained via computation of nonparametric density estimates from simulated realizations of the model. Unfortunately, the bias inherent in density estimation can cause bias in the resulting log-likelihood estimate that alters the location of its maximizer. In this paper a methodology for radically reducing this bias is developed for models with an additive error component. An illustrative example involving a stochastic model of molecular fragmentation and measurement is given.     

我国区域经济差距的空间演变趋势及其成因

文章采用空间马尔科夫链方法分析了我国区域差距和经济发展的空间效应及演变趋势,发现地理背景对经济类型转移具有显著影响,区域经济类型不会发生明显的跃迁,经济增长存在空间相关性和路径依赖性。然后,利用Kernel密度分布法提供了同时考察经济增长及区域差距的新分析框架,从经济增长分布演进的视角,分析了我国区域经济差距的原因。研究结果显示,中国经济差距扩散的动力来源于效率改进而非要素投入。     

我国男子跳高训练的新思路

分析了我国男子跳高成绩停滞不前的原因,提出教练员要有创新精神,要重视青少年基本功训练,要重视个体化训练,突出专项训练,注重心理素质训练提高参加世界大赛能力等训练新思路.     

背越式跳高运动员训练过程中恐惧心理的产生原因与克服方法

从跳高运动员的主观状态、客观因素、教练员的焦虑问题三个方面对背越式跳高训练过程中运动员恐惧心理的产生原因进行了分析,并提出了相应的克服方法和建议,为背越式跳高理论和训练提供参考。     

数学教育评价的马尔科夫链模型应用

通过分析学生两次高等代数的测试成绩,应用马尔可夫链分析法考虑学生的原始状态,并在同一标准下把学生的原始成绩分成相同的等级,确定出状态空间,然后得出转移概率和转移矩阵,根据马尔可夫链模型的平稳性及遍历性求出极限向量,并进行比较判断结果表明,学生学习状态的转移概率仅与教学质量和教学条件有关,而与学生的基础无关。运用该方法对教学质量的评价说明,学生的进步情况比学生成绩本身更重要,客观地看出教师在教学方法上的优势和劣势,并使教师获取教学的反馈信息,以对自己的教学行为进行反思和调整,促进教师教学水平的提高。     

Keep Your Friends Close? Supply Chain Design and Disruption Risk

In this article, we evaluate the relationship between supply chain design decisions and supply chain disruption risk. We explore two supply chain design strategies: (i) the dispersion of supply chain partners to reduce supply chain disruption risk versus (ii) the co‐location of supply chain partners to reduce supply chain disruption risk. In addition, we assess supply chain disruption risk from three perspectives: the inbound material flow from the supplier (supply side), the internal production processes (internal), and the outbound material flow to the customer (customer side) as a disruption can occur at any of these locations. We measure disruption risk in terms of stoppages in flows, reductions in flow, close calls (disruptions that were prevented at the last minute), disruption duration (time until normal operation flow was restored), and the spread of disruptions all the way through the supply chain. We use seemingly unrelated regression (SUR) to analyze our data, finding that lead times, especially supply side lead times, are significantly associated with higher levels of supply chain disruption risk. We find co‐location with suppliers appears to have beneficial effects to the reduction of disruption duration, and, overall supply side factors have a higher impact when it comes to supply chain disruption risk than comparable customer side factors.