Transformer in navigation: Diverse government roles for open innovation in China's high-speed rail

Based on a longitudinal case study of China's high-speed rail embedded in the emerging economy context, we focus on what role the government plays and how that matters to open innovation (OI) for competitive advantage. By linking the OI literature with the national political institutions literature to motivate our research question, we propose a statist-based OI view to differentiate diverse government roles, investigating how government adopts roles in a combined way to push OI in stages. Our findings suggest that government is an important strategic decision-maker for OI. Specifically, the government plays various roles as commander, protector, cultivator, and intermediator, reflecting state activism derived from national political institutions, to construct institutional-level OI for domestic OI activities, and inbound and outbound OI across national borders. We find government can deliberately and strategically use its diverse roles in a combined way to push OI for competitive advantage through the industrial evolutionary process over time. Our study contributes to the OI literature and integrates the strategic management literature with the study of OI to provide new insights to explain the origins of competitive advantage from the state perspective.     

基于混合加权距离的毕达哥拉斯模糊TOPSIS多属性决策方法研究

针对决策信息为毕达哥拉斯模糊集的多属性决策问题，提出了一种基于混合加权测度的TOPSIS决策方法。在分析了现有距离测度方法不足的基础上，首先给出了一种新的毕达哥拉斯模糊距离测度——毕达哥拉斯模糊有序加权距离（PFOWD），并研究了该测度权重的确定方法；在PFOWD基础上，进一步提出了毕达哥拉斯模糊混合加权距离（PFHWD），同时探讨了其特征和与现有毕达哥拉斯模糊测度的关系；最后提出了一种基于PFHWD测度的毕达哥拉斯模糊TOPSIS多属性方法，并用实例验证了方法的有效性和可行性。     

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.     

中国特色社会主义制度自信：基本依据·价值意蕴·实现路径

中国特色社会主义制度是立足世情、国情、民情和党情建立起来的制度体系。从历史选择到制度实践构成了我们在新时代夯实制度自信的基本依据；从共同富裕到人的全面发展彰显了制度自信特有的价值意蕴。站在新的发展阶段，既需要大胆借鉴古今中外制度建设的有益成果，又务必坚持在实践基础上以理论创新推动制度创新，以制度借鉴、制度实践和制度创新进一步坚定制度自信。     

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.     

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.     

Coalition for a Healthier Community: Lessons learned and implications for future work

The Coalition for a Healthier Community (CHC) initiative was implemented to improve the health and well-being of women and girls. Underpinning CHC is a gender-based focus that uses a network of community partners working collaboratively to generate relevant behavior change and improved health outcomes. Ten programs are trying to determine whether gender-focused system approaches are cost-effective ways to address health disparities in women and girls. Programs implemented through coalitions made up of academic institutions, public health departments, community-based organizations, and local, regional, and national organizations, are addressing health issues such as domestic violence, cardiovascular disease prevention, physical activity, and healthy eating. Although these programs are ongoing, they have made significant progress. Key factors contributing to their early success include a comprehensive needs assessment, robust coalitions, the diversity of populations targeted, programs based on findings of the needs assessments, evaluations taking into consideration the effect of gender, and strong academic–community partnerships. A noteworthy impact of these programs has been their ability to shape and impact public, social, and health policies at the state and local levels. However, there have been challenges associated with the implementation of such a complex program. Lessons learned are discussed in this paper.     

An approximation of logarithmic functions in the regression setting

We consider a method of moments approach for dealing with censoring at zero for data expressed in levels when researchers would like to take logarithms. A Box–Cox transformation is employed. We explore this approach in the context of linear regression where both dependent and independent variables are censored. We contrast this method to two others, (1) dropping records of data containing censored values and (2) assuming normality for censored observations and the residuals in the model. Across the methods considered, where researchers are interested primarily in the slope parameter, estimation bias is consistently reduced using the method of moments approach.     

雄安新区科技企业创新能力评价研究

科技企业是实现科技创新的驱动者和科技成果转化的重要载体,也是推动研究开发的重要参与者,科学评价科技企业创新能力有助于企业自身不断发展壮大。在分析国内外新区科技企业创新驱动发展相关理论研究的基础上,从研发投入、研发基础、研发效益和现代科技四个角度,运用层次分析法构建科技企业创新能力评价系统,并通过实证分析说明评价系统的可靠性;根据评价系统测算出现阶段雄安新区科技企业创新能力,通过与成熟新区科技企业的比较,发现其短板和不足,力图为决策者科学合理评价、管理科技企业创新发展提供有益参考。     

Modeling spatial overdispersion via the generalized Waring point process

Modeling spatial overdispersion requires point process models with finite‐dimensional distributions that are overdisperse relative to the Poisson distribution. Fitting such models usually heavily relies on the properties of stationarity, ergodicity, and orderliness. In addition, although processes based on negative binomial finite‐dimensional distributions have been widely considered, they typically fail to simultaneously satisfy the three required properties for fitting. Indeed, it has been conjectured by Diggle and Milne that no negative binomial model can satisfy all three properties. In light of this, we change perspective and construct a new process based on a different overdisperse count model, namely, the generalized Waring (GW) distribution. While comparably tractable and flexible to negative binomial processes, the GW process is shown to possess all required properties and additionally span the negative binomial and Poisson processes as limiting cases. In this sense, the GW process provides an approximate resolution to the conundrum highlighted by Diggle and Milne.