Stochastic comparisons on sample extremes of dependent and heterogenous observations

Stochastic comparison on order statistics from heterogeneous-dependent observations has been paid lots of attention recently. This paper devotes to investigating the ordering properties of order statistics from dependent observations. We derive the usual stochastic order for sample minimums and the second smallest order statistic, the dispersive order and the star order for minimums of samples having proportional hazards and Archimedean survival copulas. Similar ordering results are also obtained for maximums and the second largest order statistic of samples having proportional reversed hazards and Archimedean copulas. Several examples illustrating the main results are presented as well.     

Stochastic analysis of covariance when the error distribution is long-tailed symmetric

In this study, we consider stochastic one-way analysis of covariance model when the distribution of the error terms is long-tailed symmetric. Estimators of the unknown model parameters are obtained by using the maximum likelihood (ML) methodology. Iteratively reweighting algorithm is used to compute the ML estimates of the parameters. We also propose new test statistic based on ML estimators for testing the linear contrasts of the treatment effects. In the simulation study, we compare the efficiencies of the traditional least-squares (LS) estimators of the model parameters with the corresponding ML estimators. We also compare the power of the test statistics based on LS and ML estimators, respectively. A real-life example is given at the end of the study.     

Bayesian variable selection approach to a Bernstein polynomial regression model with stochastic constraints

This paper provides a Bayesian estimation procedure for monotone regression models incorporating the monotone trend constraint subject to uncertainty. For monotone regression modeling with stochastic restrictions, we propose a Bayesian Bernstein polynomial regression model using two-stage hierarchical prior distributions based on a family of rectangle-screened multivariate Gaussian distributions extended from the work of Gurtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]]. This approach reflects the uncertainty about the prior constraint, and thus proposes a regression model subject to monotone restriction with uncertainty. Based on the proposed model, we derive the posterior distributions for unknown parameters and present numerical schemes to generate posterior samples. We show the empirical performance of the proposed model based on synthetic data and real data applications and compare the performance to the Bernstein polynomial regression model of Curtis and Ghosh [7 S.M. Curtis and S.K. Ghosh, A variable selection approach to monotonic regression with Bernstein polynomials, J. Appl. Stat. 38 (2011), pp. 961–976. doi: 10.1080/02664761003692423[Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]] for the shape restriction with certainty. We illustrate the effectiveness of our proposed method that incorporates the uncertainty of the monotone trend and automatically adapts the regression function to the monotonicity, through empirical analysis with synthetic data and real data applications.     

A Rejection Principle for Sequential Tests of Multiple Hypotheses Controlling Familywise Error Rates

We present a unifying approach to multiple testing procedures for sequential (or streaming) data by giving sufficient conditions for a sequential multiple testing procedure to control the familywise error rate (FWER). Together, we call these conditions a ‘rejection principle for sequential tests’, which we then apply to some existing sequential multiple testing procedures to give simplified understanding of their FWER control. Next, the principle is applied to derive two new sequential multiple testing procedures with provable FWER control, one for testing hypotheses in order and another for closed testing. Examples of these new procedures are given by applying them to a chromosome aberration data set and finding the maximum safe dose of a treatment.     

中国城市化进程效率估算及影响因素分析

本文基于2008—2012年中国285个地级市的数据,运用双边随机前沿模型测算了中国城市化进程的前沿水平以及实际水平相对前沿水平的偏离程度。结果发现,下偏效应大于上偏效应,即中国城市化进程整体表现为滞后,并且发现投资作用和政府作用在影响城市化进程中的表现为负向,另外城市化进程也表现出地区间的不平衡。最后根据本文的发现,提出相应的建议。     

熟人社会的空间秩序

村落空间是村落历史的沉淀。不同于熟人社会研究的关系取向,文章引入空间生产的视角,基于南方村落的空间图式阐释熟人社会的空间秩序。“祠堂-阳宅”的平面空间图式定义了农民自然生命升华为道德生命的日常生活路径,而“庙宇-阴宅”的纵向空间图式定义了农民应对日常生活断裂的宗教生活模式。风水的空间实践镶嵌于村落空间图式,并促进了村落空间的结构化和立体化,而村落空间结构维系了村落熟人社会的公共性。可见,熟人社会的空间秩序是空间结构生产与社会秩序生产的统一。现代性力量逐渐抽离了村落空间的历史底蕴,释放了空间权利意识,导致村落空间结构破裂与熟人社会秩序紊乱。立足村庄社会的空间治理是突破村庄空间利益困局,实现乡村振兴的重要方式。     

论动因竞争对语序自由度的影响——以Hawkins长度顺序和可移动原则为例

流行于语言学教科书中的一个观点是:语序的自由度跟形态丰富程度成正比。这种观点有很大的局限。引入类型学“动因竞争”这个概念来讨论Hawkins提出的影响名词短语修饰语语序的“可移动原则”和“长度顺序原则”,可以得出结论:语序动因的运用程度是影响语序自由度的重要因素,只是该因素长期被忽视了,而“语序动因运用规律”则可说明此问题。     

以目的与背景知识为双条件的逻辑AKC

首先,我们构造以目的和背景知识为双条件的条件句系统AKC,给出它的一些证明论结果。其次,我们引入有序邻域语义,给出描述AKC的特征公理和推理规则的框架条件,证明AKC相对这些框架条件是框架可靠的。最后,我们证明AKC相对这些框架条件也是框架完全的。     

“和”的字源及涵义辨析

“和”源于饮食、音乐等感官的调和相应,具有感官之和、性情之和、政教之和、生命之和与天人之和等不同层次的涵义。“和”、“同”并论、“和”、“中”并论,但“和”不等于“同”,“和”也不等于“中”。“和”的思想在当代具有重要的意义。