Improved Estimates and Confidence Intervals for Regenerative Simulation

In regenerative simulation, one frequently requires an estimate or a confidence interval for the ratio of the means of the two components of a certain bivariate random quantity. Estimation of this ratio has been studied by many authors and most recently by Asmussen and Rydén(2010 Asmussen, S., Rydén, T. (2010). A note on skewness in regenerative simulation. Communications in Statistics—Simulation and Computation 40: 45–57.[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). Here, we propose an estimate that performs better than the best of the known estimates. Our estimate is also lot simpler than the best known estimate.     

Static Hedging of Geometric Average Asian Options with Standard Options

In this article, we first establish a theorem that represents the price of an Asian option in terms of standard European options with a shorter term and different strikes. Then using Gauss–Hermite numerical integration, we discretize our theorem so as to use Monte Carlo simulation to examine the error of the static hedging under the Black–Scholes model and the Merton jump-diffusion model. For ease of comparison, we also provide the error of the dynamic hedging. The numerical results show that the static hedging strategy performs better than the dynamic one under both models.     

Finite-sample Resampling-based Combined Hypothesis Tests,with Applications to Serial Correlation and Predictability

This article suggests Monte Carlo multiple test procedures which are provably valid in finite samples. These include combination methods originally proposed for independent statistics and further improvements which formalize statistical practice. We also adopt the Monte Carlo test method to noncontinuous combined statistics. The methods suggested are applied to test serial dependence and predictability. In particular, we introduce and analyze new procedures that account for endogenous lag selection. A simulation study illustrates the properties of the proposed methods. Results show that concrete and nonspurious power gains (over standard combination methods) can be achieved through the combined Monte Carlo test approach, and confirm arguments in favor of variance-ratio type criteria.     

Priority Rules for Multi‐Task Due‐Date Scheduling under Varying Processing Costs

We study the scheduling of multiple tasks under varying processing costs and derive a priority rule for optimal scheduling policies. Each task has a due date, and a non‐completion penalty cost is incurred if the task is not completely processed before its due date. We assume that the task arrival process is stochastic and the processing rate is capacitated. Our work is motivated by both traditional and emerging application domains, such as construction industry and freelance consulting industry. We establish the optimality of Shorter Slack time and Longer remaining Processing time (SSLP) principle that determines the priority among active tasks. Based on the derived structural properties, we also propose an effective cost‐balancing heuristic policy and demonstrate the efficacy of the proposed policy through extensive numerical experiments. We believe our results provide operators/managers valuable insights on how to devise effective service scheduling policies under varying costs.     

Path to “Stardom” in Globally Distributed Hybrid Teams: An Examination of a Knowledge‐Centered Perspective using Social Network Analysis

Although distributed teams have been researched extensively in information systems and decision science disciplines, a review of the literature suggests that the dominant focus has been on understanding the factors affecting performance at the team level. There has however been an increasing recognition that specific individuals within such teams are often critical to the team's performance. Consequently, existing knowledge about such teams may be enhanced by examining the factors that affect the performance of individual team members. This study attempts to address this need by identifying individuals who emerge as “stars” in globally distributed teams involved in knowledge work such as information systems development (ISD). Specifically, the study takes a knowledge‐centered view in explaining which factors lead to “stardom” in such teams. Further, it adopts a social network approach consistent with the core principles of structural/relational analysis in developing and empirically validating the research model. Data from U.S.–Scandinavia self‐managed “hybrid” teams engaged in systems development were used to deductively test the proposed model. The overall study has several implications for group decision making: (i) the study focuses on stars within distributed teams, who play an important role in shaping group decision making, and emerge as a result of a negotiated/consensual decision making within egalitarian teams; (ii) an examination of emergent stars from the team members’ point of view reflects the collective acceptance and support dimension decision‐making contexts identified in prior literature; (iii) finally, the study suggests that the social network analysis technique using relational data can be a tool for a democratic decision‐making technique within groups.     

浅谈半刚性基层沥青路面轴载季节修正系数

考虑到不同季节轴栽作用效果的差异，本文以目前在公路上广泛应用的半刚性基层沥青路面为研究对象，在分析季节对路面材料设计参数影响的基础上。主要从土基回弹模量季节变化对路表弯沉的影响来确定路面轴栽季节修正系数．     

基于计算机的学习者评价框架

提出了一个基于计算机的学习者评价框架,指出了此框架的重要意义,还给出了一些基于计算机的学习者评价框架的技术实现实例.     

任务型教学模式在中国学生英语虚拟语气教学中的应用

语法是语言的一个不可缺少的组成部分,其中虚拟语气是中国英语学习者的一个重点和难点,传统的3P语法教学法效果较差,任务型教学模式被认为是能提高学生中介语水平和语言能力的一个行之有效的方法。通过教学实践探究了语法教学中应用任务型教学模式的可行性和有效性。基于测试收集的数据,采用SPSS 统计软件进行数据分析,分析结果表明接受任务型教学模式的学生通过虚拟语气的课堂教学后在认知学术语言水平（CALP）方面对虚拟语气的掌握优于接受传统语法教学法的学生,期望本研究能对教师在日常语法教学中设计和使用任务型教学模式有所启示。     

关联理论与广告效果

广告作为一种应用语言 ,不仅具有很高的商业价值和欣赏价值 ,同时它的语言研究价值也日益受到人们的重视。从认知语用学的角度出发 ,依据关联理论 ,对广告的认知推理过程加以分折 ,并结合语境效果和最佳关联的产生条件 ,探讨信息关联对广告效果的影响