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.     

An Examination of the Effect of Continuous Quality Improvements on Optimal Pricing for Durable Goods*

This paper investigates the nature of optimal prices for a durable good in the presence of continuous quality improvements. The analysis of optimal prices is based on a nonlinear dynamic model of sales response that relates price, quality, average life of a product and the persistence of quality perceptions. Numerical solutions to the model are derived by employing the generalized reduced gradient algorithm. The results show that optimal price depends on the persistence of quality perceptions and the average life of a product (an aspect of quality). The analysis of optimal results affirms results based on other models and provides insights on the influence that quality has on optimal pricing. The implications of the results and suggestions for future research are discussed.     

农业节水灌溉技术扩散过程中的影响因素分析

农业节水灌溉技术对于提高农业灌溉用水的利用率非常重要,为了使农业节水灌溉技术更好的扩散,本文对农业节水灌溉技术扩散过程进行了详细分析。通过对农业节水灌溉技术扩散过程中的扩散源、被扩散的节水灌溉技术、扩散技术采用者等影响扩散的因素进行分析,以期为农村地区农业节水灌溉技术扩散效果的提高提供帮助。     

法院如何回应民意:一种法学方法论上的解决方案

围绕司法与民意的讨论,目前各界存在原因认知、概念认知、价值认知上的观点错位,影响民众对司法回应的肯定.作为法律适用机关的法院在回应民意上具有不同于政治机关的特点,必须以一种理性、中立的态度审慎地回应民意,做到有理有节、有所为有所不为.在回应过程中,应当根据民意的特点着重处理法意与民意、社会民意与权力意志之间的矛盾冲突关系,为此,必须在回应民意的时机选择与方法操作上遵循司法活动的基本规律.通过认定疑难案件的程序操作,使得外在的民意得以稳定化,以程序阻隔"舆论暴力";通过法律论证的操作,以普遍化、一致性与融贯性三原则检验与提升民意本身的合理性程度,以方法压缩个人恣意.法院的回应是对成熟且理性的民意的回应,建立在个案基础上的回应能逐步挽回民众对法院的信任.     

政府应急处理中的人权保障——以比例原则为视角

作为我国行政应急管理的基本法,《突发事件应对法》通过构建紧急状态下国家和公民活动规则的基本原则与框架,尊重和保持非常时期下的宪法民主制度和公民权利,从而维系宪法保障人权的基本价值。有效处理突发事件要求政府在紧急状态中享有应急特权,而应急权的行使当然应以比例原则来加以规制。我国的《突发事件应对法》对比例原则进行了明确规定,通过其合理适用,比例原则将有力促进政府应急处置权与公民权利之间的平衡,实现法治国家在紧急状态下的人权保障。     

WTO框架下农业补贴的法律适用问题研究

在WTO框架下规范补贴问题的有适用所有货物贸易的一般补贴规则——《补贴与反补贴措施协议》,以及专门规范农业补贴问题的《农业协议》。在涉及农业补贴的具体法律适用上,根据累加适用原则,在农业补贴问题上同样要适用一般补贴规则。然而累加适用,一般补贴规则和《农业协议》会产生冲突,此时根据有效解释原则、《农业协议》第21.1条和特别法优于一般法原则,《农业协议》应该优先于一般补贴规则适用。     

发展重庆市养殖业的影响因素分析

面对世界经济一体化的到来、中国加入WTO、西部大开发战略的实施 ,重庆养殖业国际化是必然趋势。通过分析重庆养殖业发展的影响因素 ,认为以大城市带大农村特色的重庆直辖市发展养殖业既有机会 ,又有挑战。应运用现代养殖科学技术 ,提高养殖产品质量 ,充分利用绿箱保护政策 ,获取市场机会 ,确保重庆养殖产品能顺利进入市场 ,让重庆养殖业立足市内、国内市场 ,着眼于国际市场健康持续发展     

新疆高校中亚留学生汉语学习需求调查研究——以新疆大学为例

采用问卷调查的方法,以新疆大学国际文化交流学院中亚留学生为调查样本,对其汉语学习需求进行了探索性实证研究,得出以下主要结论：中亚留学生汉语水平的目标主要分为初级和中高级两个层次,应细化学生需求,提供区别性教学对策。中亚留学生汉语学习时间较短,应采用速成强化教学模式。汉字占用了过多的学习资源,应采用“语文分开、集中识字”的教学模式。中亚留学生汉语学习符合二语习得一般规律,应加强泛读课型和习得性教学活动的研究。新疆对外汉语教学与中亚留学生的目标需求之间存在着差距,应加强教学资源的建设。     

Bayesian credible intervals for response surface optima

In response surface methodology, one is usually interested in estimating the optimal conditions based on a small number of experimental runs which are designed to optimally sample the experimental space. Typically, regression models are constructed from the experimental data and interrogated in order to provide a point estimate of the independent variable settings predicted to optimize the response. Unfortunately, these point estimates are rarely accompanied with uncertainty intervals. Though classical frequentist confidence intervals can be constructed for unconstrained quadratic models, higher order, constrained or nonlinear models are often encountered in practice. Existing techniques for constructing uncertainty estimates in such situations have not been implemented widely, due in part to the need to set adjustable parameters or because of limited or difficult applicability to constrained or nonlinear problems. To address these limitations a Bayesian method of determining credible intervals for response surface optima was developed. The approach shows good coverage probabilities on two test problems, is straightforward to implement and is readily applicable to the kind of constrained and/or nonlinear problems that frequently appear in practice.