Strategies of interaction

Our general theoretical orientation proposes that individuals in a task-oriented group setting have a variety of strategies or rules by which they decide how to organize their exchange behavior. Status consistency, reciprocity, and sex roles are examined as possible bases for interpersonal strategies. Hypotheses derived from this examination are tested in a two-person game experiment, in which independent variables are payoff matrix (mixed motive vs. cooperative), information transfer (one-way vs. two-way), and social context (married couples, mixed sex strangers, and same sex strangers). The dependent variables are number of cooperative choices and reciprocity, the latter being measured by the correlation between number of cooperative choices by one subject in a five-trial block and the number of cooperative choices by his/her partner in the previous five-trial block.     

苏俄、共产国际与陈炯明接触始末

20世纪20年代初,苏俄、共产国际在东方战略的指导下,通过各种渠道在华寻找政治同盟。南方实力派陈炯明资深的革命经历、对新生苏俄的赞美和尊崇、反帝爱国的政治态度曾一度使他成为苏俄、共产国际选择的联合对象。然而事实证明陈炯明并非莫斯科式的革命者,"六一六"事件发生后,陈氏对莫斯科和中共的态度也急转直下,苏俄、共产国际结束了联合陈炯明的尝试,从而转向与孙中山的合作。     

Who will be idol? The importance of social networks for winning on reality shows

This paper examines, both theoretically and empirically, the effect of social networks and belonging to minority groups (or race) on the probability of winning in reality television shows. We develop a theoretical model that studies viewer behavior by presenting a framework of competition between two contestants from two different groups. The results are examined empirically using unique contestant data from the highly popular reality show “A Star Is Born”, the Israeli counterpart of “American Idol”. Our main finding is that social networks and belonging to minority groups play key roles in the contestant's victory. While the effect of belonging to a minority group is positive, the social network effect is U-shaped. Beyond the world of reality TV, this paper sheds light on the general behavior of social networks as well.     

Do ‘soft law’ board gender quotas work? Evidence from a natural experiment

In a new era of “open governance”, in which societal and corporate change is taking place, 15 predominantly European countries, including Spain, enacted board gender quotas to increase the share of women on boards. In this paper, we explore the effectiveness of the European Union’s first “soft” quota – the 2007 Spanish Gender Equality Act recommending all large public and private Spanish firms to appoint a target of 40 percent of each gender to serve as board directors by 2015. The Act provides an incentive in that quota compliant firms may receive a preference for the tendering of public contracts. We draw on institutional and resource dependency theories to motivate the first empirical test of a “soft” quota which is distinct from Norway’s “hard law” board gender quota, and more similar to the proposed EU-wide quota. Using a large novel panel of 767 Spanish firms and 2786 firm-year observations from 2005 to 2014, we exploit the Spanish Act as a natural experiment and employ a difference-in-differences model. We find that less than nine percent of targeted firms fully comply with the quota. Firms that depend on public contracts are significantly more likely to increase female representation, although quota compliant firms do not actually benefit from the Act’s potential incentive. The results highlight the Spanish government’s lack of commitment to the quota, and that the quota’s normative obligations did not trigger the adoption of gender-balanced boards.     

基于C语言的压电式喷墨打印机驱动程序设计

压电式喷墨打印机是现在市面上最流行的打印机之一。首先分析了压电式喷墨打印机的工作原理,接着测试压电式喷墨打印机,并绘制出其工作时序图,剖析其逻辑关系,在此基础之上利用STM32F105开发板和IAR Embedded Workbench For ARM5.40开发工具搭建开发环境,完成了压电式喷墨打印机的驱动程序的研发,测试表明达到了预期效果。     

十六大以来中共新闻思想的创新与发展

十六大以来,中国共产党的新闻思想随时代的发展而不断发展、创新。时代发展所带来的社会生活和新闻实践的变迁,是党的新闻思想发展的不竭动力之一;而党的执政理念和治国方略的与时俱进,则是党的新闻思想发展的另一不竭动力。创新与发展主要体现在如下六个方面:将以人为本作为新闻宣传工作的根本要求;倡导按照新闻传播规律办事;由舆论引导发展为提高舆论引导能力/传播能力;以新闻传媒为重要渠道,实行信息公开;统筹国内国际两个方面,加大对外传播力度;对互联网坚持加强建设与加强管理并重。     

Central limit theorems for LS estimators in the EV regression model with dependent measurements

In this paper, we consider the simple linear errors-in-variables (EV) regression models: η_i=θ+βx_i+ε_i,ξ_i=x_i+δ_i,1≤i≤n, where θ,β,x₁,x₂,… are unknown constants (parameters), (ε₁,δ₁),(ε₂,δ₂),… are errors and ξ_i,η_i,i=1,2,… are observable. The asymptotic normality for the least square (LS) estimators of the unknown parameters β and θ in the model are established under the assumptions that the errors are m-dependent, martingale differences, ?-mixing, ρ-mixing and α-mixing.     

Almost sure central limit theorem for the maxima and sums of stationary Gaussian sequences

In this paper, we proved an almost sure central limit theorem for the maxima (after centered at the sample mean) and the partial sums of standardized stationary Gaussian sequences under some conditions related to the convergence rate of covariance functions, which extended the existing results.     

Testing homogeneity in a multivariate mixture model

Testing homogeneity is a fundamental problem in finite mixture models. It has been investigated by many researchers and most of the existing works have focused on the univariate case. In this article, the authors extend the use of the EM‐test for testing homogeneity to multivariate mixture models. They show that the EM‐test statistic asymptotically has the same distribution as a certain transformation of a single multivariate normal vector. On the basis of this result, they suggest a resampling procedure to approximate the P‐value of the EM‐test. Simulation studies show that the EM‐test has accurate type I errors and adequate power, and is more powerful and computationally efficient than the bootstrap likelihood ratio test. Two real data sets are analysed to illustrate the application of our theoretical results. The Canadian Journal of Statistics 39: 218–238; 2011 © 2011 Statistical Society of Canada     

A review of statistical methods in imaging genetics

With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi‐modality imaging, genetic, neurocognitive and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function and brain‐related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention and treatment of numerous complex brain‐related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high‐dimensional imaging phenotypes and high‐dimensional genetic data, a big data squared (BD²) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high‐dimensional nature of BD², various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel‐wise approaches, reduced rank regression, mixture models and group sparse multi‐task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research. The Canadian Journal of Statistics 47: 108–131; 2019 © 2019 Statistical Society of Canada