水工美学的理论框架

人文水利要解决的是水与文化、水利与人的关系问题。水工美学的研究对象是山、水、水工建筑物的序结构,是美学四载体形、光、与声、色的最优配置。我们应以诗心、书骨、画眼、园趣、乐感、文蕴、哲理去建构水工美学。     

城市大型热源联网运行管网布置方案及水力工况比较

城市大型热源联网运行具有明显的社会、环境和经济效益．能有效地提高集中供热的安全性和质量．本文在分析联网运行制约因素的基础上．提出了一种新的联网方案：串联热源的同程式供热管网方案．该方案通过相互间加热网水．联网的各热源将各自的热最转移到城市的集中供热管网．各供热区间的管网布置为同程式．本文对其进行了初步的分析和研究．并着重讨论厂其在水力工况和水力稳定性方面的性质．     

农机服务发展与粮食生产效率研究:2004—2016——基于变系数随机前沿分析

采用“两步法”研究了农机服务发展与中国粮食生产效率的关系.基于2004-2016年全国31省份粮食生产投入产出的面板数据,利用变系数随机前沿分析方法测算了中国粮食生产的技术变化、技术效率以及全要素生产率,然后构建农机服务发展影响粮食生产效率的OLS回归模型.结果表明:(1)31省份粮食生产的投入要素产出弹性和技术效率存在明显差异,2008年粮食技术变化最为明显,粮食全要素生产率的增长在2008年高达5%.(2)农机服务在2008年以后对粮食全要素生产率的增长具有显著的促进作用,农机服务对粮食TFP的作用存在一定的滞后效应.(3)农机服务发展与劳动产出弹性具有替代关系,与化肥和机械产出弹性具有互补关系.在三大粮食作物中,小麦机械化程度最高,水稻和玉米的机械化程度还有待提升.     

不定次重复的古诺模型博弈及其仿真研究

重复次数不确定的重复博弈比无限次重复博弈更符合客观实际。提出了不定次重复的古诺模型及不同的博弈策略；通过计算机仿真的方法，根据设计的仿真方案对不定次重复的古诺模型博弈过程进行了仿真，得到了不同贴现因子条件下各种仿真方案的厂商得益；对各方案的结果进行了分析比较，得到了统计意义上的临界贴现因子和均衡策略；阐释了仿真结果的输入弹性的概念，并用其分析了不同仿真方案的结果差异的原因。     

A robust approach to longitudinal data analysis

The author introduces robust techniques for estimation, inference and variable selection in the analysis of longitudinal data. She first addresses the problem of the robust estimation of the regression and nuisance parameters, for which she derives the asymptotic distribution. She uses weighted estimating equations to build robust quasi‐likelihood functions. These functions are then used to construct a class of test statistics for variable selection. She derives the limiting distribution of these tests and shows its robustness properties in terms of stability of the asymptotic level and power under contamination. An application to a real data set allows her to illustrate the benefits of a robust analysis.     

ON SELECTING VARIABLES AND ASSESSING THEIR PERFORMANCE IN LINEAR DISCRIMINANT ANALYSIS

Linear discriminant analysis between two populations is considered in this paper. Error rate is reviewed as a criterion for selection of variables, and a stepwise procedure is outlined that selects variables on the basis of empirical estimates of error. Problems with assessment of the selected variables are highlighted. A leave-one-out method is proposed for estimating the true error rate of the selected variables, or alternatively of the selection procedure itself. Monte Carlo simulations, of multivariate binary as well as multivariate normal data, demonstrate the feasibility of the proposed method and indicate its much greater accuracy relative to that of other available methods.     

A bias analysis of Weibull models under heaped data

Retrospectively collected duration data are often reported incorrectly. An important type of such an error is heaping—respondents tend to round-off or round-up the data according to some rule of thumb. For two special cases of the Weibull model we study the behaviour of the ‘naive estimators’, which simply ignore the measurement error due to heaping, and derive closed expressions for the asymptotic bias. These results give a formal justification of empirical evidence and simulation-based findings reported in the literature. Additionally, situations where a remarkable bias has to be expected can be identified, and an exact bias correction can be performed.     

Slack-variable models versus Scheffé's mixture models

Slack-variable models are compared against Scheffé's polynomial model for mixture experiments. The notion of model equivalence and the use of various diagnostic measures provide effective tools in making such comparisons, particularly when the experimental region is highly constrained. It is demonstrated that the choice of the best fitting model, through variable selection, depends on which mixture component is selected as a slack variable, and on the size of the fitted model. In addition, the equivalence of two well-known representations of a complete mixture model is shown to be valid. Two numerical examples are presented.     

关于齐次马氏信源二元函数的一类偏差定理

利用任意信源相对熵密度偏差的概念,使用分析的方法研究了齐次马氏信源的一类强偏差定理,得出了一个二元函数的一类强偏差定理和几个极限性质.     

可编程控制器在洁净空调自动控制系统中的应用

介绍了可编程控制器(PLC)在洁净空调控制系统中应用的实例，对应用中遇到的问题提出了解决方案，并对PLC在洁净空调系统中推广使用的前景进行了分析。