大学生就业指导中风险决策的实践与模拟

为帮助大学生在就业前景不确定时做出更合理的决策,基于风险决策理论探讨了大学生的就业指导框架,并通过蒙特卡洛法来模拟其实践效果。首先,拓展出＂期望机会成本＂的概念,提出了＂期望剩余—期望效用—最大可能性—硬币＂的就业决策标准。其次,分别从大学生决策和就业指导的角度构建了4步决策分析框架和6步指导框架。最后,以对某毕业生的就业决策指导为例,展示了利用风险决策来指导大学生就业的实践与模拟效果。     

The Effects of Type I Error Rate and Power of the ANCOVA F Test and Selected Alternatives Under Nonnormality and Variance Heterogeneity

The authors sought to identify through Monte Carlo simulations those conditions for which analysis of covariance (ANCOVA) does not maintain adequate Type I error rates and power. The conditions that were manipulated included assumptions of normality and variance homogeneity, sample size, number of treatment groups, and strength of the covariate-dependent variable relationship. Alternative tests studied were Quade's procedure, Puri and Sen's solution, Burnett and Barr's rank difference scores, Conover and Iman's rank transformation test, Hettmansperger's procedure, and the Puri-Sen-Harwell-Serlin test. For balanced designs, the ANCOVA F test was robust and was often the most powerful test through all sample-size designs and distributional configurations. With unbalanced designs, with variance heterogeneity, and when the largest treatment-group variance was matched with the largest group sample size, the nonparametric alternatives generally outperformed the ANCOVA test. When sample size and variance ratio were inversely coupled, all tests became very liberal; no test maintained adequate control over Type I error.     

Data-Division–Specific Robustness and Power of Randomization Tests for ABAB Designs

This study deals with the statistical properties of a randomization test applied to an ABAB design in cases where the desirable random assignment of the points of change in phase is not possible. To obtain information about each possible data division, the authors carried out a conditional Monte Carlo simulation with 100,000 samples for each systematically chosen triplet. The authors studied robustness and power under several experimental conditions—different autocorrelation levels and different effect sizes as well as different phase lengths determined by the points of change. Type I error rates were distorted by the presence of autocorrelation for the majority of data divisions. The authors obtained satisfactory Type II error rates only for large treatment effects. The relation between the lengths of the four phases appeared to be an important factor for the robustness and power of the randomization test.     

固定利率住房抵押贷款定价研究

本文采用最小二乘回归蒙特卡洛模拟对固定利率住房抵押贷款进行定价研究。模型结合当前我国住房抵押贷款合同的具体条款和提前偿还特征,分析了住房抵押贷款的违约期权风险和违约概率,运用无风险套利原理得到住房抵押贷款的市场公允利率。结果表明,该模型可以为商业银行的房贷利率定价提供参考依据。     

保护中小股东的奇异认沽权证蒙特卡罗模拟方案设计

针对经济活动(如股改、定向增发、兼并收购、重组、整体上市和非常广泛的股权合作)中有可能存在的控股股东对中小股东利益的侵害,引入认沽权证产品,在协商的基础上设计一种多执行时机、执行价格按一定利率随时间线性增长的奇异认沽权证方案,利用蒙特卡罗模拟最小二乘法编程计算权证的模拟值,在保护中小股东利益的基础上,有助于合作中控股股东战略意图(如股改、定向增发、兼并收购、重组、整体上市等)的实施和顺利促进最广泛的双方股权合作。     

Digital Module 11: Bayesian Psychometric Modeling https://ncme.elevate.commpartners.com

In this digital ITEMS module, Dr. Roy Levy describes Bayesian approaches to psychometric modeling. He discusses how Bayesian inference is a mechanism for reasoning in a probability-modeling framework and is well-suited to core problems in educational measurement: reasoning from student performances on an assessment to make inferences about their capabilities more broadly conceived, as well as fitting models to characterize the psychometric properties of tasks. The approach is first developed in the context of estimating a mean and variance of a normal distribution before turning to the context of unidimensional item response theory (IRT) models for dichotomously scored data. Dr. Levy illustrates the process of fitting Bayesian models using the JAGS software facilitated through the R statistical environment. The module is designed to be relevant for students, researchers, and data scientists in various disciplines such as education, psychology, sociology, political science, business, health, and other social sciences. It contains audio-narrated slides, diagnostic quiz questions, and data-based activities with video solutions as well as curated resources and a glossary.     

Neumann展开Monte Carlo随机无网格伽辽金法的镗刀位移变异分析

本文用Neumann展开Monte Carlo随机无网格伽辽金法（NMC—SEFGM）进行了随机结构分析。在随机无网格伽辽金法中，所求解问题的域由分布的节点表示，并采用移动最小二乘函数近似试函数以及用罚函数法施加本质边界条件。同时利用Neumann展开法，建立了随机结构分析的Neumann展开Monte Carlo随机无网格伽辽金法，并应用于镗刀杆工作位移的变异分析。数值实例表明Neumann展开Monte Carlo随机无网格伽辽金法适用于材料变异系数大和要求精度高的随机结构分析。     

一维长程相互作用吸附模型的临界点与作用强度之间的Monte Carlo模拟研究

利用动力学Monte Carlo方法对一维长程相互作用吸附模型进行计算机模拟研究, 得出了其临界点λc与作用力程r-1-α中的α在α>1．0时具有指数关系:λc=λc0 Be-α/γ．     

“马尔可夫链蒙特卡洛”(MCMC)方法在估计IRT模型参数中的应用

本文介绍和阐述怎样运用“马尔可夫链蒙特卡洛”(MCMC)技术,并结合Bayes方法来估计IRT的模型参数。首先简要地概述了MCMC方法估计模型参数的基本原理;其次介绍MCMC方法估计模型参数的一般方法,涉及Gibbs抽样、取舍抽样、Metropolis-Hastings算法等概念和方法;最后以IRT的“二参数逻辑斯蒂”(2PL)模型为例,重点介绍了用“Gibbs范围内的M-H算法”估计项目参数(β1jβ2j)的算法过程。结束本文时还解说了MCMC方法的特点。阅读本文需具有随机过程、Markov链、Bayes方法等概率论的基本知识。     

探讨蒙特卡罗方法在解微分方程边值问题中的应用

蒙特·卡罗方法（Monte Carlo method）,也称统计模拟方法,简写MC。是由20世纪40年代美国在第二次世界大战中研制原子弹的“曼哈顿计划”中的计划成员S.M.乌拉姆和J.冯·诺伊曼首先提出。之后数学家将其命名为蒙特卡罗,它以概率理论为指导,是一种非常重要的统计方法,利用常见的伪随机数解决多种计算问题的方法。这种方法在金融工程学、宏观经济学、计算物理学等领域被广泛的应用。早在18世纪法国数学家布丰利用投针实验的方法求圆周率π,被认为是蒙特·卡罗方法的起源。