Measure of departure from symmetry for the analysis of collapsed square contingency tables with ordered categories

For square contingency tables with ordered categories, there may be some cases that one wants to analyze them by considering collapsed tables with some adjacent categories combined in the original table. This paper considers the symmetry model for collapsed square contingency tables and proposes a measure to represent the degree of departure from symmetry. The proposed measure is defined as the arithmetic mean of submeasures each of which represents the degree of departure from symmetry for each collapsed 3×3 table. Each submeasure also represents the mean of power-divergence or diversity index for each collapsed table. Examples are given.     

A generalized analysis of the dependence structure by means of ANOVA

The multiple non-symmetric correspondence analysis (MNSCA) is a useful technique for analysing the prediction of a categorical variable through two or more predictor variables placed in a contingency table. In MNSCA framework, for summarizing the predictability between criterion and predictor variables, the Multiple-TAU index has been proposed. But it cannot be used to test association, and for overcoming this limitation, a relationship with C-Statistic has been recommended. Multiple-TAU index is an overall measure of association that contains both main effects and interaction terms. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition. On the other hand, the interaction effect represents the combined effect of predictor variables on the response variable. In this paper, we propose a decomposition of the Multiple-TAU index in main effects and interaction terms. In order to show this decomposition, we consider an empirical case in which the relationship between the demographic characteristics of the American people, such as race, gender and location (column variables), and their propensity to move (row variable) to a new town to find a job is considered.     

Experimental designs for detecting synergy and antagonism between two drugs in a pre‐clinical study

The identification of synergistic interactions between combinations of drugs is an important area within drug discovery and development. Pre‐clinically, large numbers of screening studies to identify synergistic pairs of compounds can often be ran, necessitating efficient and robust experimental designs. We consider experimental designs for detecting interaction between two drugs in a pre‐clinical in vitro assay in the presence of uncertainty of the monotherapy response. The monotherapies are assumed to follow the Hill equation with common lower and upper asymptotes, and a common variance. The optimality criterion used is the variance of the interaction parameter. We focus on ray designs and investigate two algorithms for selecting the optimum set of dose combinations. The first is a forward algorithm in which design points are added sequentially. This is found to give useful solutions in simple cases but can lack robustness when knowledge about the monotherapy parameters is insufficient. The second algorithm is a more pragmatic approach where the design points are constrained to be distributed log‐normally along the rays and monotherapy doses. We find that the pragmatic algorithm is more stable than the forward algorithm, and even when the forward algorithm has converged, the pragmatic algorithm can still out‐perform it. Practically, we find that good designs for detecting an interaction have equal numbers of points on monotherapies and combination therapies, with those points typically placed in positions where a 50% response is expected. More uncertainty in monotherapy parameters leads to an optimal design with design points that are more spread out. Copyright © 2015 John Wiley & Sons, Ltd.     

我国高校科研成果绩效综合评价模型研究

在高校科研管理工作中引进绩效评价方法是时之所需,因为高校科研成果绩效评价对高校科研具有极大的导向与激励作用,它已成为众多高校科研项目获得资源支持的标准之一。对于目前我国高校科研成果绩效评价所面临的“质量”与“数量”之间的矛盾,本文将通过单项要素分析与层次聚类分析,建立各学科间的差异化评价指标体系,提供更为合理的评价方法,以达到“以评促优”的目的,促进我国高校科研良性发展。     

试论以EVA为导向的国有企业经营者业绩评价指标体系及应用——对17家中药行业上市公司经营者业绩的分析

本文在对我国国有企业经营者业绩评价指标体系运用现状分析的基础上,提出以EVA(经济增加值)为导向构建国企经营者业绩评价综合指标体系,并以中药行业国有性质的上市公司为例,进行指标体系的应用示例。     

幸福指数指标体系的构建

经济增长的终极目的是使人民生活得更幸福。但由于过去在评价经济发展时过多的关注GDP等硬数据,而对经济社会的全面发展重视不够,对人民幸福程度这一软指标关注不够,使得虽然经济增长了,但人们的幸福感不但没有随之增长甚至还有所下降。因此,研究幸福指标的经济学意义及幸福指数指标体系的构建,对决策部门制定经济发展政策、出台以人为本的相关人文关怀政策、促进社会全面和谐发展,都具有十分重要的意义。     

股指期货与现货指数收益率序列相关性研究——基于Copula函数的实证分析

沪深300股指期货推出后,其与沪深300指数的关系就引起投资者和研究者的关注。以沪深300指数和沪深300股指期货的日收益率数据为基础,运用Copula函数建立Copula-GARCH(1,1)-GED模型对两者进行相关性分析,结果表明:沪深300指数与股指期货收益率序列之间相关程度非常高,而通过比较秩相关系数的拟合情况,二元正态Copula函数更接近实际情况;在平方欧式距离的标准下,二元t-Copula模型能够更好地描述沪深300指数与沪深300股指期货日收益率序列的相关结构;两序列的尾部相关程度非常高,表明当股票市场大幅度波动时,沪深300指数与沪深300股指期货的相关程度显著提高。     

基于规模发展的零售业态区域差异实证研究

在确定零售业态规模发展评价指标基础上,进行各区域主要零售业态规模的实证研究。研究结果表明,便利店、超市等新兴业态在各地区发展不平衡现象比较突出,各地区的各业态规模发展总量与规模发展速度均表现出个体差异性。这些发展特征与各业态在我国所处的生命周期有关,而不同经济水平下的消费需求在很大程度也决定了地区业态发展程度,同时,消费需求的适应性使得各业态的要素投入在地区内发生规模转移,从而直接影响业态的规模变化。     

全要素生产率视角下的高技术产业效率提升——基于5大行业的面板数据实证分析

基于高技术产业2000-2008年的面板数据,采用非参数的Malmquist指数分析方法,对5大行业的全要素生产率、技术效率等的变化趋势进行研究,研究结果表明全要素生产率的提高主要是由于技术进步的贡献,行业间效率变化水平差异性较为明显,自主创新能力较弱。提出优化资源配置、完善自主创新体系等政策建议,以期提升我国高技术产业效率,落实国家自主创新战略。     

我国环保企业生产效率及不确定性——基于面板数据的DEA与神经网络分析

环境保护产业作为中国战略性新兴产业之一,为新一轮中国经济增长增添动力。同时,它也是中国促进可持续发展与技术发展、抓住新机遇,建设一个创新型国家的重要措施。然而,由于工业化的反作用,同时作为环保科技产业自身的特点,环保企业的生产效率从总体来说普遍不高。为了反映生产效率,建立一个考虑固定资产、管理费用、财务费用、与职工有关现金支出、总税费支出、利润等与企业环保有关的财务支出指标体系。利用DEA方法对2004-2010年各企业面板数据分析企业的生产效率,同时利用神经网络模拟预测其2011年到2015年发展前景。