回族服饰文化与伊斯兰教

自元、明以来 ,回族的服饰在不断变化 ,但其中的宗教基因却保留不变。它既表现出华夏民族的文化传承 ,又涵盖着伊斯兰宗教文化的教义、教规与伦理道德 ,在中国少数民族服饰中独树一帜 ,为具有美观、端庄、俭朴等特色且表现出民族心理及审美观念的服饰文化。回族服饰文化是宗教礼仪服饰文化与世俗服饰文化的有机结合 ,体现着回族人民的智慧与民族精神     

黑龙江渤海山城分布与特征

本文利用大量的实地考察成果 ,论述了黑龙江渤海山城的分布和主要特征     

档案信息资源开发利用研究

在信息时代的今天,信息资源已然成为社会和经济发展的重要资源。而档案就是信息资源的重要组成。伴随信息化、数字化社会建设的推进,人们逐步意识到档案信息的重要性,面对互联网日益普及,信息海量产出的形势,如何进行档案信息资源的科学开发和有效利用成为档案管理部门必须重视的问题。基于此,对新时期档案信息资源的开发利用进行深入研究很有必要。     

农业文化遗产的内涵及保护中应注意把握的八组关系

关于农业文化遗产的内涵及如何保护,学者及社会各界众说纷纭,莫衷一是。文章在梳理前 人相关研究和国际比较的基础上, 提出了农业文化遗产的完整概念及它与联合国粮农组织(FAO)“全 球重要农业遗产"(GIAHS)的区别。将农业文化遗产划分为十个大类,界定了各类农业遗产的特征与特 点。在结合国内外农业文化遗产保护实践的基础上,总结归纳了农业文化遗产保护过程中应当注意把 握的八组关系,即传统农业与现代农业的关系、遗产保护与农民利益的关系、生产功能与文化功能的关 系、保护主体与多方协调的关系、理论研究与实践推进的关系、现实保护与记忆留存的关系、政策导向与 制度建设的关系及保护主体与社会大众的关系, 提出了一些加强农业文化遗产保护的具体对策和 建议。     

“微信热”背后高校思想政治教育的冷思考

大学生是微信最忠实的拥趸,“微信热”不仅给大学生的工作和生活带来了诸多影响,同时也给高校思想政治教育工作带来了严峻挑战。微信的不可控性,使高校思想政治教育环境趋于复杂;微信立体化的社交形式,使高校思想政治教育方法受到挑战;微信的虚拟性,使高校思想政治教育过程难度加大;微信信息的“碎片化”,削弱了高校思想政治教育实效性。在“微信热”背景下,探讨其产生的深层原因,科学应对微信带来的挑战,运用微信进行“微创新”、发挥“微功能”,对大学生思想政治教育意义重大。     

“互联网+”时代高校教育与管理的变革与展望

“互联网+”时代高校传统教育与管理面临着前所未有的新形势和新挑战,在“互联网+”和高校信息化建设的时代背景下,高校必须要把握变革机遇、重塑管理理念观念、树立互联网思维,准确理解“互联网+”校园的理念和内涵,将新一代信息技术充分应用并深度融入高校教育教学、管理服务中,以构建智能化、智慧型校园为切入点,推进高校信息化校园建设,促进我国高等教育转型升级和一流大学、一流学科建设.     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

A win ratio approach to comparing continuous non‐normal outcomes in clinical trials

Clinical trials are often designed to compare continuous non‐normal outcomes. The conventional statistical method for such a comparison is a non‐parametric Mann–Whitney test, which provides a P‐value for testing the hypothesis that the distributions of both treatment groups are identical, but does not provide a simple and straightforward estimate of treatment effect. For that, Hodges and Lehmann proposed estimating the shift parameter between two populations and its confidence interval (CI). However, such a shift parameter does not have a straightforward interpretation, and its CI contains zero in some cases when Mann–Whitney test produces a significant result. To overcome the aforementioned problems, we introduce the use of the win ratio for analysing such data. Patients in the new and control treatment are formed into all possible pairs. For each pair, the new treatment patient is labelled a ‘winner’ or a ‘loser’ if it is known who had the more favourable outcome. The win ratio is the total number of winners divided by the total numbers of losers. A 95% CI for the win ratio can be obtained using the bootstrap method. Statistical properties of the win ratio statistic are investigated using two real trial data sets and six simulation studies. Results show that the win ratio method has about the same power as the Mann–Whitney method. We recommend the use of the win ratio method for estimating the treatment effect (and CI) and the Mann–Whitney method for calculating the P‐value for comparing continuous non‐Normal outcomes when the amount of tied pairs is small. Copyright © 2016 John Wiley & Sons, Ltd.     

Analysis of some ruin-related quantities in a Markov-modulated risk model

In this paper, we study the joint Laplace transform and probability generating function of some random quantities that occur in each environment state by the time of ruin in a Markov-modulated risk process. These quantities include the duration spent in each state, the number of claims and the aggregate amount of claims that occurred in each state by the time of ruin. Explicit formulae for the joint transforms, given the initial surplus, and the initial and terminal environment states, are expressed in terms of a matrix version of the scale function. Moments and covariances of these ruin-related quantities are obtained and numerical illustrations are presented. The joint transform of the duration spent in each state, the number of claims, and the aggregate amount of claims that occurred in each state by the time the surplus attains a certain level are also investigated.     

Studying Complexity of Model-based Clustering

Cluster analysis is a popular statistics and computer science technique commonly used in various areas of research. In this article, we investigate factors that can influence clustering performance in the model-based clustering framework. The four factors considered are the level of overlap, number of clusters, number of dimensions, and sample size. Through a comprehensive simulation study, we investigate model-based clustering in different settings. As a measure of clustering performance, we employ three popular classification indices capable of reflecting the degree of agreement in two partitioning vectors, thus making the comparison between the true and estimated classification vectors possible. In addition to studying clustering complexity, the performance of the three classification measures is evaluated.