Measuring technological breadth and depth of patent documents using Rao’s Quadratic Entropy

ABSTRACT

In this article we propose indices for technological breadth and depth of patent documents. We conceptually compare and contrast several indices that have been used in the existing literature, explaining main limitations. Motivated by the drawbacks of those we demonstrate how Rao’s Quadratic Entropy, a statistical index used in ecology for measuring biodiversity, can be decomposed to separately measure technological breadth and depth. The properties of breadth and depth are then investigated using patents for business data processing. For the technological domains with the highest patenting activity, we show how the novel measures of technological breadth and depth can be used to rank the patenting entities by average breadth, and identify ones with inventions that on average exceed the domain-specific average depth. Practical implications of the proposed indices are also exemplified and discussed in the context of competitor analysis for entrepreneurial ventures in the area of network security for business data processing.     

权力距离文化价值观对我国税收道德的影响——基于腐败容忍度的中介效应检验

基于世界价值观调查中国部分的微观数据，对个体权力距离文化价值观与税收道德之间的关系进行实证分析。结果表明，高权力距离文化价值观对税收道德存在显著的负向影响。使用两阶段最小二乘法解决内生性问题后，上述结论仍然成立。进一步研究发现，权力距离文化价值观主要通过腐败容忍度对税收道德产生影响，意味着腐败容忍度影响税收道德的背后动因是个体权力距离文化价值观。研究认为提高我国居民税收道德应从规范税收自由裁量权入手，通过规范权力运行、推进税收共治，同时发挥区块链技术在构建税收信用体系中的优势作用，以此驱动个体纳税意愿。     

赔付率约束下的中国保险公司效率与全要素生产率研究

运用SBM(slack-based measure)方向性距离函数分别计算了赔付率约束下的2006-2016年中国境内的财产保险公司、人身保险公司的经营无效率值,并对无效率值的来源进行分解。在此基础上运用Luenberger生产率指数计算出全要素生产率及其分解项,对中资、外资保险公司的经营效率、全要素生产率及其分解项进行了对比分析。结果表明:财险业的中资大型公司经营效率最高,人身险业的中资公司经营效率明显高于外资公司;投资收益无效率是造成中小型人身险公司和中小型财产险公司无效率的最主要原因;财险业大型公司的全要素生产率低于中小型公司,而人身险业外资公司的全要素生产率明显高于中资公司。     

Chernoff distance for doubly truncated distributions

In a recent paper, Nair et al. [Stat Pap 52:893–909, 2011] proposed Chernoff distance measure for left/right-truncated random variables and studied their properties in the context of reliability analysis. Here we extend the definition of Chernoff distance for doubly truncated distributions. This measure may help the information theorists and reliability analysts to study the various characteristics of a system/component when it fails between two time points. We study some properties of this measure and obtain its upper and lower bounds. We also study the interval Chernoff distance between the original and weighted distributions. These results generalize and enhance the related existing results that are developed based on Chernoff distance for one-sided truncated random variables.     

Does Size Matter? A Study of Risk Perceptions of Global Population Growth

The global human population now exceeds 7 billion and is projected to reach 10 billion around 2060. While population growth has been associated with certain benefits (e.g., economies of scale, technological advancements), theoretical models, probabilistic projections, and empirical evidence also indicate that this growth could increase the likelihood of many adverse events (e.g., climate change, resource shortages) and the impact of these events, as more people are exposed to the outcomes. While concerns about these issues are well‐documented in the academic literature, there is little evidence concerning the public's perceptions of the risks associated with global population growth (GPG) and how these perceptions are likely to influence related decisions. To address these issues, we conducted a U.K.‐based study that examined respondents’ risk perceptions of GPG, their willingness to embrace mitigation/precautionary behaviors, and reasons for variations in these two factors. We found that GPG is perceived as a moderate‐to‐high risk, with concerns about the increased likelihood of resource shortages, ecological damage, and violent conflict being foremost. Respondents believed that the worst effects of GPG would arrive around 2050 and would be experienced by the world's poorest people. Respondents who perceived greater levels of risk from GPG were generally those who indicated a greater willingness to embrace mitigation behaviors (e.g., reduce resource consumption) and preventative actions (e.g., support political action to limit growth). We discuss how our findings might be utilized to better manage the potential challenges associated with GPG and we suggest several directions for further research.     

Measuring departmental and overall regional performance: applying the multi-activity DEA model to Taiwan׳s cities/counties

This study measures the departmental and overall efficiency in Taiwan׳s counties/cities by applying a multi-activity data envelopment analysis (MADEA) model. The model overcomes the problems of panel data, undesirable outputs, shared inputs, and environmental variables and intertemporal efficiency changes (productivity) by applying the Malmquist–Luenberger (ML) index. We include data on the economic development, social welfare, police and security, and education departments for 20 counties/cities in Taiwan for the period 1999–2013. We find that the police security department is the most efficient in most counties/cities in the period 1999–2013, and the economic development department is the second efficient one in 2002–2005 and after 2009. Furthermore, there exist urban–rural gaps in the efficiency scores between counties and cities, between service-type and non-service-type counties/cities, and among different regions. With regard to the efficiencies over time (ML indices and their decompositions), we find that the production frontiers of the social welfare and education departments in Taiwan׳s counties/cities expanded continuously during this period. Finally, we also find that urban–rural gaps and gaps between service-type and non-service-type counties/cities exist in terms of technological changes and ML productivity indices in the social welfare and education departments. The area differences of technological changes exist in 4 departments and in overall. Our results will help the mayors of counties/cities understand the strengths and weaknesses of the regions they govern.     

基于正交投影法的动态评价方法

为了更全面评价不同对象在不同时刻的总体发展水平，本文提出了基于正交投影法的动态评价方法。首先为克服传统理想解法欧式距离计算贴近度的不足，采用"垂直距离"代替"欧式距离"计算方案的贴近度，然后考虑时间因素构建动态评价模型，分别计算待评价对象在指标值差异程度和增长程度情况下的综合评价值，最后通过二次加权法将各时间段的评价值进行综合计算得出待评价方案的综合评价结果。该方法既能计算待评价对象在任意时间的评价值，又能计算其在某特定时间段内的总体评价值。最后用实例讨论了动态评价模型中偏好参数对排序结果的灵敏度证明了其稳定性，更进一步地通过与其他动态评价方法的比较，证明了其有效性。     

Sufficient dimension reduction via distance covariance with multivariate responses

In this article, we propose a new method for sufficient dimension reduction when both response and predictor are vectors. The new method, using distance covariance, keeps the model-free advantage, and can fully recover the central subspace even when many predictors are discrete. We then extend this method to the dual central subspace, including a special case of canonical correlation analysis. We illustrated estimators through extensive simulations and real datasets, and compared to some existing methods, showing that our estimators are competitive and robust.     

Testing Independence of Covariates and Errors in Non‐parametric Regression

Consider a non‐parametric regression model Y =m (X )+ϵ , where m is an unknown regression function, Y is a real‐valued response variable, X is a real covariate, and ϵ is the error term. In this article, we extend the usual tests for homoscedasticity by developing consistent tests for independence between X and ϵ . Further, we investigate the local power of the proposed tests using Le Cam's contiguous alternatives. An asymptotic power study under local alternatives along with extensive finite sample simulation study shows that the performance of the new tests is competitive with existing ones. Furthermore, the practicality of the new tests is shown using two real data sets.     

Robust multivariate diagnostics for PLSR and application on high dimensional spectrally overlapped drug systems

ABSTRACT

Statistical methods are effectively used in the evaluation of pharmaceutical formulations instead of laborious liquid chromatography. However, signal overlapping, nonlinearity, multicollinearity and presence of outliers deteriorate the performance of statistical methods. The Partial Least Squares Regression (PLSR) is a very popular method in the quantification of high dimensional spectrally overlapped drug formulations. The SIMPLS is the mostly used PLSR algorithm, but it is highly sensitive to outliers that also effect the diagnostics. In this paper, we propose new robust multivariate diagnostics to identify outliers, influential observations and points causing non-normality for a PLSR model. We study performances of the proposed diagnostics on two everyday use highly overlapping drug systems: Paracetamol–Caffeine and Doxylamine Succinate–Pyridoxine Hydrochloride.