Conditions of Slope-Rotatability for Third-Order Polynomial Regression Models

In experimental design for response surface analysis, it is sometimes of interest to estimate the difference of responses at two points. If differences at points close together are involved, the design that reliably estimates the slope of the response surface is important. In particular, Hader and Park (1978 Hader , R. J. , Park , S. H. ( 1978 ). Slope-rotatable central composite designs . Technometrics 20 : 413 – 417 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) suggested the concept of slope-rotatability and studied slope rotatable central composite designs. Until now, many response surface designs including central composite designs have been suggested for fitting second order response surface models. However, we often need to fit third-order polynomial regression models. In this article, we suggest extended central composite designs (ECCDs) to fit third-order models and find the necessary and sufficient conditions for slope-rotatability over all directions in the third-order polynomial models.     

Some bivariate uniform distributions

Bivariate uniform distributions with dependent components are readily derived by distribution function transformations of the components of non-uniform dependent continuous bivariate random variables (X,Y). Contour plots of joint density functions show the various, and varying, forms of dependence which can arise from different distributional forms for (X,Y) and aids the choice of bivariate uniform distributions as empirical models.     

On a variance stabilizing model and its application to genomic data

In this paper, we propose a model based on a class of symmetric distributions, which avoids the transformation of data, stabilizes the variance of the observations, and provides robust estimation of parameters and high flexibility for modeling different types of data. Probabilistic and statistical aspects of this new model are developed throughout the article, which include mathematical properties, estimation of parameters and inference. The obtained results are illustrated by means of real genomic data.     

Robust Model-Based Inference for Incomplete Data via Penalized Spline Propensity Prediction

Parametric model-based regression imputation is commonly applied to missing-data problems, but is sensitive to misspecification of the imputation model. Little and An (2004 Little , R. J. A. , An , H. ( 2004 ). Robust likelihood-based analysis of multivariate data with missing values . Statistica Sinica 14 : 949 – 968 .[Web of Science ®] , [Google Scholar]) proposed a semiparametric approach called penalized spline propensity prediction (PSPP), where the variable with missing values is modeled by a penalized spline (P-Spline) of the response propensity score, which is logit of the estimated probability of being missing given the observed variables. Variables other than the response propensity are included parametrically in the imputation model. However they only considered point estimation based on single imputation with PSPP. We consider here three approaches to standard errors estimation incorporating the uncertainty due to non response: (a) standard errors based on the asymptotic variance of the PSPP estimator, ignoring sampling error in estimating the response propensity; (b) standard errors based on the bootstrap method; and (c) multiple imputation-based standard errors using draws from the joint posterior predictive distribution of missing values under the PSPP model. Simulation studies suggest that the bootstrap and multiple imputation approaches yield good inferences under a range of simulation conditions, with multiple imputation showing some evidence of closer to nominal confidence interval coverage when the sample size is small.     

Developments in Maximum Likelihood Unit Root Tests

The exact maximum likelihood estimate provides a test statistic for the unit root test that is more powerful than the usual least-squares approach. In this article, a new derivation is given for the asymptotic distribution of this test statistic that is simpler and more direct than the previous method. The response surface regression method is used to obtain a fast algorithm that computes accurate finite-sample critical values. This algorithm is available in the R package mleur that is available on CRAN. The empirical power of the new test is shown to be much better than the usual test not only in the normal case but also for innovations generated from an infinite variance stable distribution as well as for innovations generated from a GARCH(1,1) process.     

混合题型的IRT等值问题研究

我们称既有0—1评分题,又有多级评分题的试卷为混合题型试卷,简称为混合题型．以往国内对混合题型的测验等值采用的方法是将Logistic模型看成是等级反应模型或拓广的分部评分模型的特例,这种处理方式就可能忽略了项目的猜测因素．为了解决这一问题,本文将三参数的Logistic模型和GRM进行扩展得到混合模型,并针对此模型开发了相应的等值程序．另外,为了检验某次测验0—1评分项目存在猜测,而人为将其忽略所带来的误差大小,本文进行了大量的MonteCarlo模拟实验．实验结果表明,若某测验中0—1评分项目存在猜测而等值时忽略这一事实误用GRM,在绝大部分情况下都比用混合模型等值的误差大而且有显著性差异,并且等值的误差会随着猜测度的的增大而增大．     

货币供应量对股市影响的实证分析

笔者利用我国货币供应量和股票价格指数的相关数据,通过构建VAR模型,分别对牛市和熊市两种行情下货币供应量对股票价格指数的影响进行实证分析。研究表明:两种行情下,货币供应量与股票价格指数均存在长期均衡关系,在牛市行情下,货币供应量对股票价格指数的上涨具有积极的促进作用,而在熊市行情下,货币供应量对股票价格指数影响不大。     

国家与社会:明清的里社制与乡村秩序———以徽州“社会“为线索的考察

明清国家法律规定了里社制,随着里甲赋役制度的变革,里社制却逐渐废弛,被民间“社会”与民间信仰所取代。 “社会”在长期的祭祀活动中,因为社会关联形成集体意识,这种共识可对超出村庄、宗族的公共事务发布禁约,从而产生地区公共秩序。 国家法律与乡村秩序相互影响,在国家法律下行之时,乡村秩序则自生自发地生成回应型规则,认识和协调二者之关系,对于乡村治理有积极意义。     

服务不满意情境下的消费者反应类型测评及高阶因子分析

过去对于消费者不满意的研究主要关注的是服务失误和消费者抱怨.在消费者抱怨的相关研究中,学者们提出了消费者抱怨行为(CCB)、消费者反应预测(CRE)以及消费者抱怨反应(CCR)来解释消费者的抱怨行为,但对于消费者不满意之后的其它反应类型研究较少.本研究基于学者们对抱怨类型的研究以及组织行为理论中雇员反应类型(EVLN)模型的研究,探讨在消费者行为研究中,消费者对于不满意的反应类型.通过探索性因子分析和验证性因子分析,我们确定了五种消费者在不满意情景下可能有的反应类型.我们希望企业能够认识到不同的反应类型,从而提高服务质量和服务补救的有效性.     

读者·文本·召唤结构——关于大学英语阅读教学的思考

阅读文本自身所存在的大量空白构成了一个动态、开放并蕴含无限可能的话语结构,它召唤读者通过再创造对其进行建构和填充。这一理论对于大学英语阅读教学具有极大的实践意义,只有充分确立和彰显学生的主体性,尊重文本的多元性解读,促进读者与文本的视域融合并创造积极、平等和和谐的师生对话,才能真正使学生在文本意义构建中发挥建设性作用,从而将阅读知识与能力推进到一个新的高度。