Interval estimation for misclassification rate parameters in a complementary Poisson model

We investigate three interval estimators for binomial misclassification rates in a complementary Poisson model where the data are possibly misclassified: a Wald-based interval, a score-based interval, and an interval based on the profile log-likelihood statistic. We investigate the coverage and average width properties of these intervals via a simulation study. For small Poisson counts and small misclassification rates, the intervals can perform poorly in terms of coverage. The profile log-likelihood confidence interval (CI) is often proved to outperform the other intervals with good coverage and width properties. Lastly, we apply the CIs to a real data set involving traffic accident data that contain misclassified counts.     

Confidence intervals on p(y < x) for small sample sizes

When F = G^a, confidence intervals are derived and presented in graphs for p = P(Y < X), when X and Y are independent and the sample sizes are at most 25. Also, it is demonstrated via a monte carlo simulation that this is a robust procedure, when the distributions of X and Y differ by a location parameter.     

Study of the behaviour of flexible production systems using neural nets

Owing to high development and acquisition costs, production systems require very detailed analysis for pre-design specifications, and an adjustment in operating philosophies. Detailed simulation models are one of the most important instruments to achieve this goal; normally the simulator must be custom-built, based on an accurate study of the specific industrial problem. The proposed management and economical analysis uses mathematical meta-models obtained from experimental designs produced by the simulator; today the detail level and precision required is very high, so these kinds of meta-model operate very close to their limit. Here we present a new methodology based on artificial neural networks that produces new types of meta-models which can correctly represent complex industrial systems. In this work we propose an application of this methodology together with a detailed analysis of the model development phase to highlight the potential of this instrument.     

Push-Kanban – a kanban-based production control concept for job shops

Job-shop production in an individual and small-batch manufacturing environment demands producing simultaneously different products in many customised versions and in small numbers. The associated variability of the time-phased work content leads to a high degree of complexity and turbulence in the manufacturing process of such a production type. Therefore, production control becomes significantly more complicated, and many theories on how to cope fail in the real world. Despite the many technological and conceptual advances in the field of job-shop control over the past few decades, there is still a remarkable lack of practical control approaches for job-shop production. For this reason the Push-Kanban production control concept will be introduced in this article. It combines robust push scheduling along with a kanban-driven, decentralised inventory control mechanism to create a holistic control approach, integrating all production control tasks. Thus, it offers a feasible option for controlling job-shop production in industrial practice. This article examines the effectiveness of the concept by means of a simulation study based on real data.     

Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

The U.S. Department of Energy has estimated that over 50 GW of offshore wind power will be required for the United States to generate 20% of its electricity from wind. Developers are actively planning offshore wind farms along the U.S. Atlantic and Gulf coasts and several leases have been signed for offshore sites. These planned projects are in areas that are sometimes struck by hurricanes. We present a method to estimate the catastrophe risk to offshore wind power using simulated hurricanes. Using this method, we estimate the fraction of offshore wind power simultaneously offline and the cumulative damage in a region. In Texas, the most vulnerable region we studied, 10% of offshore wind power could be offline simultaneously because of hurricane damage with a 100‐year return period and 6% could be destroyed in any 10‐year period. We also estimate the risks to single wind farms in four representative locations; we find the risks are significant but lower than those estimated in previously published results. Much of the hurricane risk to offshore wind turbines can be mitigated by designing turbines for higher maximum wind speeds, ensuring that turbine nacelles can turn quickly to track the wind direction even when grid power is lost, and building in areas with lower risk.     

Bootstrap approach to test the homogeneity of order restricted mean vectors when the covariance matrices are unknown

Testing homogeneity of multivariate normal mean vectors under an order restriction when the covariance matrices are unknown, arbitrary positive definite and unequal are considered. This problem of testing has been studied to some extent, for example, by Kulatunga and Sasabuchi (1984 Kulatunga, D. D. S., Sasabuchi, S. (1984). A test of homogeneity of mean vectors against multivariate isotonic alternatives. Mem Fac Sci, Kyushu Univ Ser A Mathemat 38:151–161. [Google Scholar]) when the covariance matrices are known and also Sasabuchi et al. (2003 Sasabuchi, S., Tanaka, K., Tsukamodo, T. (2003). Testing homogeneity of multivariate normal mean vectors under an order restriction when the covariance matrices are common but unknown. Annals of Statistics. 31(5):1517–1536.[Web of Science ®] , [Google Scholar]) and Sasabuchi (2007 Sasabuchi, S. (2007). More powerful tests for homogeneity of multivariate normal mean vectors under an order restriction. Sankhya 69(4):700–716. [Google Scholar]) when the covariance matrices are unknown but common. In this paper, a test statistic is proposed and because of the main advantage of the bootstrap test is that it avoids the derivation of the complex null distribution analytically, a bootstrap test statistic is derived and since the proposed test statistic is location invariance the bootstrap p-value defined logical and some steps are presented to estimate it. Our numerical studies via Monte Carlo simulation show that the proposed bootstrap test can correctly control the type I error rates. The power of the test for some of the p-dimensional normal distributions is computed by Monte Carlo simulation. Also, the null distribution of test statistic is estimated using kernel density. Finally, the bootstrap test is illustrated using a real data.     

An Evaluation of Simulations in Developmental Disabilities (SIDD): Instructional Software that Provides Practice in Behavioral Assessment and Treatment Decisions

ABSTRACT

Simulations in Developmental Disabilities (SIDD) is a multimedia computer program designed to provide undergraduate psychology students with practice in making assessment and treatment decisions. Eighteen undergraduate psychology students participated in an experiment to test the instructional effectiveness of SIDD. Post-test scores were significantly higher in the experimental group who received training with SIDD than in a control group who did not receive training. The students also rated the software positively. Future strategies to further evaluate the software are discussed.     

An Easily Implemented Generalised Inverse Gaussian Generator

Using the ‘ratio’ method an easily implemented algorithm is derived for the generalised inverse Gaussian distribution. Computer timings and efficiency calculations show that the procedure is fast over a wide range of distribution parameter values.     

缝洞型油藏储集体分类建模方法研究

为了表征缝洞型碳酸盐岩油藏储集体类型多样、尺度差异大、空间分布规律复杂的特征，采用"分级分类、岩溶相控、多类型融合"的方法，分别对不同类型缝洞储集体的规模大小、外部几何形态及内部属性特征进行建模，建立了基于岩溶成因的融合原则，实现了不同类型缝洞储集体的融合，并通过新钻井验证了模型可靠性。结果表明，该方法适用于此类强非均质性油藏的地质建模，能够有效表征不同类型储集体及属性参数的空间分布特征。基于模型，细化了XX单元的储量构成，其中，溶洞储量占63.1%，溶蚀孔洞储量占34.5%，大尺度裂缝储量占2.4%。该模型在油藏数值模拟中取得了较好的拟合效果和模拟结果。     

基于系统动力学模型的发动机再制造经济性研究

从生产和物流成本出发,对发动机再制造经济性进行分析。基于系统动力学建立发动机再制造全过程关键因素及成本分析模型,研究了不同批量、不同库存策略、不同管理方法下的再制造成本,最后通过系统动力学软件Vensim对成本模型进行模拟仿真,并得出结论:随着产能的扩大,为降低回收成本,应在原有网络的基础上进一步扩大回收网络;因缺货带来的影响非常明显,应采取面向订单生产模式的库存策略以减少缺货成本。