再论企业自知识能力及其管理

根据先前的研究 ,从企业核心能力的角度阐述了自知识能力的本质特征 ,即动态的特征、宽泛的特征、深层次的特征。分析了扼杀企业自知识能力的因素 ,包括企业文化因素、组织结构设计因素、分析性思维因素、策略性决策因素、激励性体系因素等五个方面。在此基础上提出了提升企业自知识能力的对策。     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

农村居民看病难、看病贵原因实证分析

利用扩展的线性支出系统模型(ELES),采用最小二乘法估计计算了我国农村居民的短期医疗服务需求价格弹性。研究结果表明:农村居民的短期医疗服务需求价格缺乏弹性。由于农村居民的短期医疗服务需求价格缺乏弹性,加上我国目前医疗体制存在的以药养医和医疗机构的逐利行为以及医疗市场缺乏有效的监管等问题,导致农村居民看病难、看病贵。解决这个问题,需要国家增加对卫生领域的投入,降低农民看病时自费的比例,增强卫生市场的竞争性,适当使医疗卫生资源向农村地区倾斜,并逐渐把医疗机构办成非盈利机构。     

Backfitting Random Varying-Coefficient Models with Time-Dependent Smoothing Covariates

Abstract.  In this paper, we propose a random varying-coefficient model for longitudinal data. This model is different from the standard varying-coefficient model in the sense that the time-varying coefficients are assumed to be subject-specific, and can be considered as realizations of stochastic processes. This modelling strategy allows us to employ powerful mixed-effects modelling techniques to efficiently incorporate the within-subject and between-subject variations in the estimators of time-varying coefficients. Thus, the subject-specific feature of longitudinal data is effectively considered in the proposed model. A backfitting algorithm is proposed to estimate the coefficient functions. Simulation studies show that the proposed estimation methods are more efficient in finite-sample performance compared with the standard local least squares method. An application to an AIDS clinical study is presented to illustrate the proposed methodologies.     

施工企业危机管理及其经济分析

危机管理的目的是为了避免和减少危机的损害,是一场"防御性的战斗",也是一项系统工程。论文结合竞争激烈的施工企业市场实际,从施工企业危机管理入手,阐述了施工企业危机管理目前的共性内容,并通过分析对危机管理的重要意义、危机管理效果和经济效果的衡量等问题,进一步通过施工企业危机管理的经济分析对危机管理投入、所得的界定,研究了危机管理投入的量化及归一化,以及危机管理所得的量化及归一化问题,得出单位投入所带来的整体稳定态势,其值越大表明危机管理越有效。可为施工企业开展此项工作提供一定的理论指导。     

对基础研究投入问题的思考

文章首先从公共财政的角度对基础研究的投入进行分析,其次从布什和斯托克斯的科研模式角度对基础研究的投入进行分析。在此基础上对加大我国基础研究的投入提出了相关建议。     

Local influence in multilevel models

The authors study the local influence of observations in multilevel regression models. To this end, they perturb simultaneously the variances, responses and design matrix. To measure the local change caused by these perturbations, they use generalized Cook statistics for the fixed and random parameter estimates. Closed form local influence measures also allow them to assess the joint influence of various observations. They suggest a simple computation method and illustrate their results using two examples.     

Quantifying uncertainty in the biospheric carbon flux for England and Wales

Summary.  A crucial issue in the current global warming debate is the effect of vegetation and soils on carbon dioxide (CO₂) concentrations in the atmosphere. Vegetation can extract CO₂ through photosynthesis, but respiration, decay of soil organic matter and disturbance effects such as fire return it to the atmosphere. The balance of these processes is the net carbon flux. To estimate the biospheric carbon flux for England and Wales, we address the statistical problem of inference for the sum of multiple outputs from a complex deterministic computer code whose input parameters are uncertain. The code is a process model which simulates the carbon dynamics of vegetation and soils, including the amount of carbon that is stored as a result of photosynthesis and the amount that is returned to the atmosphere through respiration. The aggregation of outputs corresponding to multiple sites and types of vegetation in a region gives an estimate of the total carbon flux for that region over a period of time. Expert prior opinions are elicited for marginal uncertainty about the relevant input parameters and for correlations of inputs between sites. A Gaussian process model is used to build emulators of the multiple code outputs and Bayesian uncertainty analysis is then used to propagate uncertainty in the input parameters through to uncertainty on the aggregated output. Numerical results are presented for England and Wales in the year 2000. It is estimated that vegetation and soils in England and Wales constituted a net sink of 7.55 Mt C (1 Mt C = 10¹² g of carbon) in 2000, with standard deviation 0.56 Mt C resulting from the sources of uncertainty that are considered.     

改进灰色预测在油田多目标规划决策中的应用

建立了S油田勘探、开发、炼化、机械、公用工程等多个部门和全局的非线性多级目标优化规划模型,应用关联分析、改进灰色预测、回归分析求取规划模型的约束方程并线性化;编制了相应的计算软件,使之快速预测和优化油田各部门“九五”各年的投资和产值;并将优化结果与油田过去或计划值加以对比,给油田规划决策带来一定的参考。     

Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.