国家创新体系视角下的国家创新能力测度述评——理论方法、数据基础及中国实践

文章从国家创新体系角度,对创新能力测度相关的理论基础、方法工具、科技统计调查及中国的研究与实践进行全面梳理和分析。国家创新能力测度的发展与实践表明:国家创新体系理论(NIS理论)以系统论为指导全面考察创新活动的发生机制,为准确测度国家创新能力提供了有力支撑;不同时期形成的创新能力测度方法与当时占主流地位的创新理论密切相关;当前占主导地位的测度方法可分为建模计量法、综合指标法和DEA效率评价法三类,都是以NIS理论为支撑的;规范的科技统计调查是准确测度创新能力的前提,OECD是这方面的典范,其经验为发展中国家提供了参考依据;中国学者在测度方法的探索上有诸多独到之处,但国内科技统计调查工作仍有待进一步规范。     

Quality_Family Deployment: A New Perspective in Determining Priority Importance for Improving Work Performance in Organization

Determining priority importance is a matter of concerns among the organization to improve their performance. One of the important aspects that should be considered by the organization is management of human resources, comprising of members who have their own family life. In this paper, we deliver a new perspective for organization to provide priority importance for their members with respect to family matters in order to inculcate the sense of belonging in the organization. To be effective in considering family matters in the organizational policy making, closer look of family characteristics are required. The idea of translating several family characteristics as quality variables and applying the quality function deployment (QFD) method to these variables could present a new way of improving the decision making in the organization by considering the process of decision in the family. Quality function deployment for family (QFDF) produces a friendly interpretation of a highly complex and intangible matters around family life; thus, making the assessment of a family easier. QFD is applied on the data gathered from a questionnaire survey based on 1,213 families in West Malaysia, Malaysia. Twelve variables are identified as voice of family, where seven of them are found to have the highest priority of importance. These variables could also be translated into quality variables in the context of organization, the strategy of improvement for the family could be interpreted as strategy for improvement in the organization. Furthermore, the results of this study provide suggestion to improve actions for managing human resource. In the discussion, three most high ranked variables from both perspectives of family and organization are considered.     

对外汉语教材生词英语译释和汉语词语学习偏误

通过对对外汉语初级教材中生词英语译释存在的译释方式不妥、译释不够准确、忽略生词的附属义、忽略生词的用法等方面问题进行例举分析,指出这些问题会诱发英语干扰,导致语际负迁移,造成词语理解错误,以致留学生学习汉语词汇时容易出现语义及语法层面的偏误。     

中国建设投资与经济增长关系的计量模型与分析

文章对我国建设投资和国民经济之间的关系进行了定量的分析 ,建立了能反映二者之间长期均衡和短期波动关系的误差修正模型 ,从而得到近阶段国民经济增长对我国建设投资的弹性系数 ,反映了建设投资对国民经济增长的贡献水平。从模型分析的结果显示 ,无论是固定资产投资 ,还是建筑安装投资 ,GDP对它们的弹性系数都大大超过它们各自在 GDP中所占的比重。这表明建设投资能够高效率地拉动国民经济的增长 ,是刺激经济活动的重要手段之一。     

双重政策目标下的货币政策传导机制研究

运用误差修正模型和协整检验对我国货币政策传导机制和货币政策有效性问题进行实证研究。结果表明,在二元政策目标框架下,货币供给具有较强的外生性,中央银行对通货膨胀的厌恶性正逐渐超过对经济增长的偏好性;相对于信贷渠道而言,货币渠道对货币政策传导的贡献度不高,货币政策主要通过信货渠道来影响总需求。     

SEMIPARAMETRIC REGRESSION AND GRAPHICAL MODELS

Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian 'inference engines', such as bugs and vibes , facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology.     

Forecast mean squared error reductionin the VAR(1) process

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.     

Generalized Augmentation to Control the False Discovery Exceedance in Multiple Testing

Abstract.  A new multiple testing procedure, the generalized augmentation procedure (GAUGE), is introduced. The procedure is shown to control the false discovery exceedance and to be competitive in terms of power. It is also shown how to apply the idea of GAUGE to achieve control of other error measures. Extensions to dependence are discussed, together with a modification valid under arbitrary dependence. We present an application to an original study on prostate cancer and on a benchmark data set on colon cancer.     

Estimation in capture–recapture models when covariates are subject to measurement errors and missing data

For capture–recapture models when covariates are subject to measurement errors and missing data, a set of estimating equations is constructed to estimate population size and relevant parameters. These estimating equations can be solved by an algorithm similar to the EM algorithm. The proposed method is also applicable to the situation when covariates with no measurement errors have missing data. Simulation studies are used to assess the performance of the proposed estimator. The estimator is also applied to a capture–recapture experiment on the bird species Prinia flaviventris in Hong Kong. The Canadian Journal of Statistics 37: 645–658; 2009 © 2009 Statistical Society of Canada     

Nonparametric density deconvolution by weighted kernel estimators

Nonparametric density estimation in the presence of measurement error is considered. The usual kernel deconvolution estimator seeks to account for the contamination in the data by employing a modified kernel. In this paper a new approach based on a weighted kernel density estimator is proposed. Theoretical motivation is provided by the existence of a weight vector that perfectly counteracts the bias in density estimation without generating an excessive increase in variance. In practice a data driven method of weight selection is required. Our strategy is to minimize the discrepancy between a standard kernel estimate from the contaminated data on the one hand, and the convolution of the weighted deconvolution estimate with the measurement error density on the other hand. We consider a direct implementation of this approach, in which the weights are optimized subject to sum and non-negativity constraints, and a regularized version in which the objective function includes a ridge-type penalty. Numerical tests suggest that the weighted kernel estimation can lead to tangible improvements in performance over the usual kernel deconvolution estimator. Furthermore, weighted kernel estimates are free from the problem of negative estimation in the tails that can occur when using modified kernels. The weighted kernel approach generalizes to the case of multivariate deconvolution density estimation in a very straightforward manner.