Nonparametric control charts based on runs and Wilcoxon-type rank-sum statistics

In this article, we introduce three new distribution-free Shewhart-type control charts that exploit run and Wilcoxon-type rank-sum statistics to detect possible shifts of a monitored process. Exact formulae for the alarm rate, the run length distribution, and the average run length (ARL) are all derived. A key advantage of these charts is that, due to their nonparametric nature, the false alarm rate (FAR) and in-control run length distribution is the same for all continuous process distributions. Tables are provided for the implementation of the charts for some typical FAR values. Furthermore, a numerical study carried out reveals that the new charts are quite flexible and efficient in detecting shifts to Lehmann-type out-of-control situations.     

Statistical inference for α-series process with the inverse Gaussian distribution

Statistical inferences for the geometric process (GP) are derived when the distribution of the first occurrence time is assumed to be inverse Gaussian (IG). An α-series process, as a possible alternative to the GP, is introduced since the GP is sometimes inappropriate to apply some reliability and scheduling problems. In this study, statistical inference problem for the α-series process is considered where the distribution of first occurrence time is IG. The estimators of the parameters α, μ, and σ² are obtained by using the maximum likelihood (ML) method. Asymptotic distributions and consistency properties of the ML estimators are derived. In order to compare the efficiencies of the ML estimators with the widely used nonparametric modified moment (MM) estimators, Monte Carlo simulations are performed. The results showed that the ML estimators are more efficient than the MM estimators. Moreover, two real life datasets are given for application purposes.     

Rank and response: A field experiment on peer information and water use behavior

Perception of peer rank, or how we perform relative to our peers, can be a powerful motivator. While research exists on the effect of social information on decision making, there is less work on how ranked comparisons with our peers influence our behavior. This paper outlines a field experiment conducted with 3896 households in Castro Valley, California, which uses household mailers with various forms of social information and peer rank messaging to motivate water conservation. The experiment tests the effect of a visible peer rank on water use, and how the competitive framing of rank information influences behavioral response. The results show that households with relatively low or high water use in the pre-treatment period responded differently to how rank information was framed. I find that a neutrally-framed peer rank caused a small “boomerang effect” (i.e., an increase in average water use) for low water use households, but this effect was eliminated by competitive framing. At the same time, a competitively-framed peer rank demotivated high water use households, increasing their average water use over the full period of the experiment. This result is supported by evidence that the competitive frame on rank information increased water use for households who ranked “last” in the peer group – a detrimental “last place effect” from competitively-framed rankings.     

Rent-seeking and competitive preferences

In this experiment, I examine the extent to which competitive social preferences can explain over-bidding in rent-seeking contests. The Human treatment is a standard two-player contest. In the Robot treatment, a single player bids against a computerized player, eliminating potential social preference motives. The results show no difference in bids between treatments at the aggregate level. Further analysis shows evidence of heterogeneous treatment effects between impulsive and reflective subjects. Moreover, impulsive subjects are more likely than reflective subjects to deviate qualitatively from the shape of the theoretical best response function.     

复合期权在R＆D项目投资评价中的应用

如何评价R&D项目投资已成为目前理论界关注的一个热点。对R&D项目投资的基本评价方法———贴现现金流法 (DCF)已越来越不能适应R&D项目管理的复杂性和不确定性 ,而实物期权方法以其能够灵活评价不确定性条件下的投资项目未来的增长机会的特点 ,受到越来越多的关注。借鉴了Geske提出的复合期权模型 ,将R&D项目看作一个复合期权 ,建立了R&D项目的投资评价模型     

经济增速回调初步触底，企稳基础仍需巩固--2016-2017年经济形势分析与展望

当前中国经济正处在增速换挡的拐点附近,这是一个不同于一般经济周期性变化的拐点。从高增长平稳进入中高增长,既包括增长水平的稳定,也包括增长模式的转变。目前,支持经济增长稳定的因素已明显增多,但仍有不稳定、不确定因素;增长模式转变则正处于爬坡过坎的关键时期。必须继续坚持需求和供给两方面措施并举,短期目标和长期目标结合,精准稳需求、稳增长,着力推进供给侧结构性改革,不断巩固经济中高速增长的基础,提高增长的含金量。     

陕西省物流业的投入产出分析研究

运用投入产出法对陕西省三次产业和物流业做投入产出、产业关联、产业波及效应等进行分析,计算出各产业部门的直接消耗系数、列昂惕夫逆矩阵、直接前向关联指数和直接后向关联指数、影响力和感应度系数。得出陕西省物流业与其他三次产业的技术经济关系,最后根据陕西省物流业存在的一些问题提出促进陕西物流业发展的建议和对策。