Interval estimation for conformance proportions of multiple quality characteristics

A conformance proportion is an important and useful index to assess industrial quality improvement. Statistical confidence limits for a conformance proportion are usually required not only to perform statistical significance tests, but also to provide useful information for determining practical significance. In this article, we propose approaches for constructing statistical confidence limits for a conformance proportion of multiple quality characteristics. Under the assumption that the variables of interest are distributed with a multivariate normal distribution, we develop an approach based on the concept of a fiducial generalized pivotal quantity (FGPQ). Without any distribution assumption on the variables, we apply some confidence interval construction methods for the conformance proportion by treating it as the probability of a success in a binomial distribution. The performance of the proposed methods is evaluated through detailed simulation studies. The results reveal that the simulated coverage probability (cp) for the FGPQ-based method is generally larger than the claimed value. On the other hand, one of the binomial distribution-based methods, that is, the standard method suggested in classical textbooks, appears to have smaller simulated cps than the nominal level. Two alternatives to the standard method are found to maintain their simulated cps sufficiently close to the claimed level, and hence their performances are judged to be satisfactory. In addition, three examples are given to illustrate the application of the proposed methods.     

Understanding cultural singularities of ‘Indianness’ in an intercultural business setting

Analysing data from the Indian information technology (IT) industry, this paper advances an understanding of cultural singularities of ‘Indianness’. The research context of an intercultural meeting place of IT and business process outsourcing firms’ overseas subsidiaries, Belgium in this case, allows the authors to identify 10 cultural singularities that typify ‘Indianness’. This ethnographic, reflexive study is further validated by employing Ghoshal's ‘smell of the place’ metaphor through the authors collective experiences as consultants and researchers, and builds and extends upon the popular cultural dimension frameworks for understanding intercultural business and management. Existing cultural dimensions do not sufficiently describe the contemporary intercultural dynamics that typically take place in workplaces, especially so in offshore and outsourcing environments. A provisional set of parameters for understanding Indian culture, with its relevant impact on business life (customs and manners), business processes and business deliverables are proposed in this study.     

Multivariate normal mean–variance mixture distribution based on Birnbaum–Saunders distribution

A multivariate normal mean–variance mixture based on a Birnbaum–Saunders (NMVMBS) distribution is introduced and several properties of this new distribution are discussed. A new robust non-Gaussian ARCH-type model is proposed in which there exists a relation between the variance of the observations, and the marginal distributions are NMVMBS. A simple EM-based maximum likelihood estimation procedure to estimate the parameters of this normal mean–variance mixture distribution is given. A simulation study and some real data are used to demonstrate the modelling strength of this new model.     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.     

Approximation of the Bivariate Renewal Function

Recently, Kambo and his co-researchers (2012) proposed a method of approximation for evaluating the one-dimensional renewal function based on the first three moments. Their method is simple and elegant, which gives exact values for well-known distributions. In this article, we propose an analogous method for the evaluation of bivariate renewal function based on the first two moments of the variables and their joint moment. The proposed method yields exact results for certain widely used bivariate distributions like bivariate exponential distribution, bivariate Weibull distributions, and bivariate Pareto distributions. An illustrative example in the form of a two-dimensional warranty problem is considered and comparisons of our method are made with the results of other models.     

政府向社会力量购买服务改革的制度障碍及对策

政府向社会力量购买服务不仅是推动政府职能转变的重要着力点,而且是扶持社会力量发展的重要措施。各地在推动这项改革过程中取得了很大成效,但也存在着制度设计不严密、制度衔接不顺畅等问题和障碍。将这项改革深入推进下去,需要坚持有层次地放开政府资源,加强对整个购买过程的动态管理以及建立政府购买公共服务改革同其他改革之间的联动机制等多项措施的推动。     

Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes

We propose a sparse coefficient estimation and automated model selection procedure for autoregressive processes with heavy-tailed innovations based on penalized conditional maximum likelihood. Under mild moment conditions on the innovation processes, the penalized conditional maximum likelihood estimator satisfies a strong consistency, O_P(N^?1/2) consistency, and the oracle properties, where N is the sample size. We have the freedom in choosing penalty functions based on the weak conditions on them. Two penalty functions, least absolute shrinkage and selection operator and smoothly clipped average deviation, are compared. The proposed method provides a distribution-based penalized inference to AR models, which is especially useful when the other estimation methods fail or under perform for AR processes with heavy-tailed innovations [Feigin, Resnick. Pitfalls of fitting autoregressive models for heavy-tailed time series. Extremes. 1999;1:391–422]. A simulation study confirms our theoretical results. At the end, we apply our method to a historical price data of the US Industrial Production Index for consumer goods, and obtain very promising results.     

A new S2 control chart using repetitive sampling

A new S² control chart is presented for monitoring the process variance by utilizing a repetitive sampling scheme. The double control limits called inner and outer control limits are proposed, whose coefficients are determined by considering the average run length (ARL) and the average sample number when the process is in control. The proposed control chart is compared with the existing Shewhart S² control chart in terms of the ARLs. The result shows that the proposed control chart is more efficient than the existing control chart in detecting the process shift.     

基于工作过程的高职商务英语课程体系构建研究

针对目前我国高职商务英语课程体系存在的问题,在社会和市场广泛调研的基础上,从典型工作任务归纳、学习领域课程开发、学习情境设计和配套设施建设四个方面提出高职商务英语课程体系构建方案。     

大数据与政府治理能力提升:机遇、挑战与展望

随着社会问题复杂程度的不断增加,大数据应用为政府解决棘手问题、提升治理能力、应对挑战提供了新思路,但也带来了一定程度的适应难题。因此,探析大数据在我国的政府治理实践中如何得到合理应用、有效推动治理能力提升需要结合国内外成功经验,从机遇和挑战两个维度进行理解。分析发现,大数据可以推动政府治理协同化、政府决策科学化、数据管理规范化,并会遇到数据壁垒、专业能力欠缺以及政策法规滞后等方面的阻碍。研究意义在于分析大数据对政府治理能力提升的作用,为未来我国政府在治理实践中更好地利用大数据技术提供参考。