利用人口普查数据测量社会距离的方法探讨

社会学概念“社会距离”从博格达斯量表开始,其测量的一直是群体（族、阶层）之间的主观心理距离,反映的是群体间文化差异或群体间相互接受程度。为了回应塔尔达和拉梅尔对社会距离概念客观性的要求,本文借鉴重新定义距离这个概念,反映不同社会阶层客观的社会差别,并利用广州市2000年人口普查数据和2005年1％人口抽样调查数据来测量广州市不同阶层（群体）的社会距离,解释广州市社会阶层（群体）关系的变化,说明社会变迁。     

Rényi entropy properties of records

We provide bounds for Rényi entropy of records. We also show that the Rényi entropy ordering of random variables determines the Rényi entropy ordering of their respective records. We characterize exponential distribution by maximization of Rényi entropy under some conditions. We show that Rényi distance between distribution of records and parent distribution is distribution free.     

Assessing departure from dose linearity under a repeated measures incomplete block design

Dose proportionality/linearity is a desirable property in pharmacokinetic studies. Various methods have been proposed for its assessment. When dose proportionality is not established, it is of interest to evaluate the degree of departure from dose linearity. In this paper, we propose a measure of departure from dose linearity and derive an asymptotic test under a repeated measures incomplete block design using a slope approach. Simulation studies show that the proposed method has a satisfactory small sample performance in terms of size and power.     

心理距离影响下的人称指示借用现象

本文旨在从语用学的角度研究心理距离和人称指示借用用法之间的关系。     

Micro-structure in micro-neighborhoods: A new social distance measure,and its effect on individual and aggregated perceptions of crime and disorder

This study links social network methodology with the social disorganization literature to test the effect of block-level social distance on neighborhood perceived crime and disorder. Employing a unique study design that allows creating matrices of social distance (based on demographic characteristics) between 11 residents on each of over 650 blocks at three time points, we find that more socially distant residents perceive more disorder than their neighbors. Consistent with the bridging social capital literature, overall social distance in the block has a curvilinear relationship with perceived crime. And blocks with two cohesive subgroups, based on social distance, have lower levels of perceived disorder.     

The Moderating Role of Power Distance on the Reaction of Consumers to the CEO as a Spokesperson During a Product Harm Crisis: Insights From China and South Korea

During a crisis the corporate message is not the only issue facing the company. The role of the spokesperson is an under-researched area which is examined in this paper. In studies conducted in South Korea and China we examine the reaction of consumers to the CEO as a spokesperson during a product harm crisis. We find in both countries that consumer responses to the CEO was contingent on the consumers' level of power distance. When consumers had high levels of power distance they had higher future purchase intentions when compared with consumers who had low levels of power distance when the CEO was the spokesperson during the crisis. In addition, in a study conducted in South Korea we find that higher levels of power distance generate increased levels of brand trust when the CEO is the spokesperson, which in turn increases future purchase intentions. Our studies have important theoretical and managerial implications which are discussed in the paper.     

Classification of form under heterogeneity and non-isotropic errors

A number of areas related to learning under supervision have not been fully investigated, particularly the possibility of incorporating the method of classification into shape analysis. In this regard, practical ideas conducive to the improvement of form classification are the focus of interest. Our proposal is to employ a hybrid classifier built on Euclidean Distance Matrix Analysis (EDMA) and Procrustes distance, rather than generalised Procrustes analysis (GPA). In empirical terms, it has been demonstrated that there is notable difference between the estimated form and the true form when EDMA is used as the basis for computation. However, this does not seem to be the case when GPA is employed. With the assumption that no association exists between landmarks, EDMA and GPA are used to calculate the mean form and diagonal weighting matrix to build superimposing classifiers. As our findings indicate, with the use of EDMA estimators, the superimposing classifiers we propose work extremely well, as opposed to the use of GPA, as far as both simulated and real datasets are concerned.     

The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses

In this study, classical and robust principal component analyses are used to evaluate socioeconomic development of regions of development agencies that give service on the purpose of decreasing development difference among regions in Turkey. Due to the high differences between development levels of regions outlier problem occurs, hence robust statistical methods are used. Also, classical and robust statistical methods are used to investigate if there are any outliers in data set. In classic principal component analyse, the number of observations must be larger than the number of variables. Otherwise determinant of covariance matrix is zero. In Robust method for Principal Component Analysis (ROBPCA), a robust approach to principal component analyse in high-dimensional data, even if the number of variables is larger than the number of observations, principal components are obtained. In this paper, firstly 26 development agencies are evaluated with 19 variables by using principal component analysis based on classical and robust scatter matrices and then these 26 development agencies are evaluated with 46 variables by using the ROBPCA method.     

Bias‐reduced marginal Akaike information criteria based on a Monte Carlo method for linear mixed‐effects models

In linear mixed‐effects (LME) models, if a fitted model has more random‐effect terms than the true model, a regularity condition required in the asymptotic theory may not hold. In such cases, the marginal Akaike information criterion (AIC) is positively biased for (?2) times the expected log‐likelihood. The asymptotic bias of the maximum log‐likelihood as an estimator of the expected log‐likelihood is evaluated for LME models with balanced design in the context of parameter‐constrained models. Moreover, bias‐reduced marginal AICs for LME models based on a Monte Carlo method are proposed. The performance of the proposed criteria is compared with existing criteria by using example data and by a simulation study. It was found that the bias of the proposed criteria was smaller than that of the existing marginal AIC when a larger model was fitted and that the probability of choosing a smaller model incorrectly was decreased.