Sickness and disability policies: Reform paths in OECD countries between 1990 and 2014

We analysed sickness and disability policies for the working‐age population in a number of OECD countries, between the years 1990 and 2014. Existing evidence suggests that there has been a broad shift in focus from passive income maintenance to employment incentives and reintegration policies. We have updated detailed policy scores provided by the OECD to estimate model‐based country clusters. Our results indicate that countries have pursued different types of reforms consisting of a combination of integration and compensation measures. The reforms of recent decades have led to the emergence of a distinct cluster of Northern and Continental European countries characterised by a combination of strong employment‐oriented policies and comparatively high social protection levels. An analysis of recent reforms shows a continued expansion of measures that foster employment as well as instances of retrenchment in the compensation dimension. Diversity of policy settings across country groups, however, remains substantial.     

New Tests of Spatial Segregation Based on Nearest Neighbour Contingency Tables

Abstract.  The spatial clustering of points from two or more classes (or species) has important implications in many fields and may cause segregation or association, which are two major types of spatial patterns between the classes. These patterns can be studied using a nearest neighbour contingency table (NNCT) which is constructed using the frequencies of nearest neighbour types. Three new multivariate clustering tests are proposed based on NNCTs using the appropriate sampling distribution of the cell counts in a NNCT. The null patterns considered are random labelling (RL) and complete spatial randomness (CSR) of points from two or more classes. The finite sample performance of these tests are compared with other tests in terms of empirical size and power. It is demonstrated that the newly proposed NNCT tests perform relatively well compared with their competitors and the tests are illustrated using two example data sets.     

Cosine similarity-based clustering and dynamic reputation trust aware key generation scheme for trusted communication on social networking

Social networking sites (SNSs) make it possible to connect people and they can communicate with others. Due to the lack of privacy mechanisms, the users in SNSs are vulnerable to some kinds of attacks. Security and privacy issues have become critically important with the fast expansion of SNSs. Most network applications such as pervasive computing, grid computing and P2P networks can be viewed as multi-agent systems which are open, anonymous and dynamic in nature. Moreover, most of the existing reputation trust models (RTMs) do not depend on any clustering structures. The clustering structures are used to effectively calculate the trustworthiness of the network nodes. In this paper, a novel cosine similarity-based clustering and dynamic reputation trust aware key generation (CSBC-DRT) scheme is proposed. For better faced clustering, a cosine similarity measure is estimated for all the nodes on the network. Based on the similarity measure among the nodes, the network nodes are clustered into disjoint groups. The RTM is built in this proposed scheme. Here, an improved MD5 algorithm is explored for key generation and key verification. After the key verification, the trusted measures such as reputation value, positive edge and negative edge values are computed to formulate the trusted network. The proposed scheme performs better than the existing RTM, which provides trusted communication in social networks.     

我国县级以上城市规模等级分布的省际差异性分析

本文分析并计算了表征省际城市规模、城市规模等级分布差异性特征的指标;运用聚类分析法,对我国各个省区的城市规模等级分布进行分析,考察了各省区城市规模等级分布的动态变化,并对其进行了一些分析,提出了一些建议。     

Clustering objects on subsets of attributes (with discussion)

Summary.  A new procedure is proposed for clustering attribute value data. When used in conjunction with conventional distance-based clustering algorithms this procedure encourages those algorithms to detect automatically subgroups of objects that preferentially cluster on subsets of the attribute variables rather than on all of them simultaneously. The relevant attribute subsets for each individual cluster can be different and partially (or completely) overlap with those of other clusters. Enhancements for increasing sensitivity for detecting especially low cardinality groups clustering on a small subset of variables are discussed. Applications in different domains, including gene expression arrays, are presented.     

A joint model for hierarchical continuous and zero-inflated overdispersed count data

Many applications in public health, medical and biomedical or other studies demand modelling of two or more longitudinal outcomes jointly to get better insight into their joint evolution. In this regard, a joint model for a longitudinal continuous and a count sequence, the latter possibly overdispersed and zero-inflated (ZI), will be specified that assembles aspects coming from each one of them into one single model. Further, a subject-specific random effect is included to account for the correlation in the continuous outcome. For the count outcome, clustering and overdispersion are accommodated through two distinct sets of random effects in a generalized linear model as proposed by Molenberghs et al. [A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010;25:325–347]; one is normally distributed, the other conjugate to the outcome distribution. The association among the two sequences is captured by correlating the normal random effects describing the continuous and count outcome sequences, respectively. An excessive number of zero counts is often accounted for by using a so-called ZI or hurdle model. ZI models combine either a Poisson or negative-binomial model with an atom at zero as a mixture, while the hurdle model separately handles the zero observations and the positive counts. This paper proposes a general joint modelling framework in which all these features can appear together. We illustrate the proposed method with a case study and examine it further with simulations.     

On clustering shape data

ABSTRACT

Among the statistical methods to model stochastic behaviours of objects, clustering is a preliminary technique to recognize similar patterns within a group of observations in a data set. Various distances to measure differences among objects could be invoked to cluster data through numerous clustering methods. When variables in hand contain geometrical information of objects, such metrics should be adequately adapted. In fact, statistical methods for these typical data are endowed with a geometrical paradigm in a multivariate sense. In this paper, a procedure for clustering shape data is suggested employing appropriate metrics. Then, the best shape distance candidate as well as a suitable agglomerative method for clustering the simulated shape data are provided by considering cluster validation measures. The results are implemented in a real life application.     

基于粗糙集的聚类方法研究

粗糙集理论是一种新型的处理含糊和不确定性知识的数学工具。聚类是知识发现中的热点研究问题之一。目前,大部分聚类算法所面向的数据的属性是数值型的,少部分也针对属性是符号型的。该文提出一种基于粗糙集的新聚类方法,它能够处理数值属性与符号属性相混合的数据的聚类问题。实例说明,该方法是有效的。     

基于分布模型的层次聚类算法

提出了一种新的层次聚类算法,先对数据集进行采样,以采样点为中心吸收邻域内的数据点形成子簇,再根据子簇是否相交实现层次聚类。在层次聚类过程中,重新定义了簇与簇之间的距离度量,并以此为基础建立堆结构。利用估计数据点总体分布的思想,证明该算法将逼近最优解。实验结果表明,算法的聚类效果大大优于现有的聚类算法。     

辽宁省产业金融的发展现状及问题分析

在产业转型升级和产业与金融集聚融合的背景下,如何正确选择辽宁产业金融发展之路成为社会各界关注的焦点。从产业发展状况、金融服务能力、产融结合三个方面分析辽宁省发展产业金融过程中存在的问题,结合辽宁省自身的资源状况和历史特点探究其成因,据此提出适合辽宁省产业金融发展的思路及对策。