Generalized information matrix tests for copulas

We propose a family of goodness-of-fit tests for copulas. The tests use generalizations of the information matrix (IM) equality of White and so relate to the copula test proposed by Huang and Prokhorov. The idea is that eigenspectrum-based statements of the IM equality reduce the degrees of freedom of the test’s asymptotic distribution and lead to better size-power properties, even in high dimensions. The gains are especially pronounced for vine copulas, where additional benefits come from simplifications of score functions and the Hessian. We derive the asymptotic distribution of the generalized tests, accounting for the nonparametric estimation of the marginals and apply a parametric bootstrap procedure, valid when asymptotic critical values are inaccurate. In Monte Carlo simulations, we study the behavior of the new tests, compare them with several Cramer–von Mises type tests and confirm the desired properties of the new tests in high dimensions.     

Third-Person Effect,Religiosity and Support for Censorship of Satirical Religious Cartoons

The violent attack on the staff of Charlie Hebdo and an earlier plot to kill cartoonists from the Danish newspaper Jyllands-Posten raise the issue of censorship of satirical religious cartoons to avoid potential retaliatory violence from those offended by the cartoons. With third-person effect (TPE) as the theoretical underpinning, this study examined the relationship among TPE, religiosity, and support for censorship of religious satirical cartoons. Data were collected in May 2015. A national sample of participants (N=374) was acquired through Amazon’s Mechanical Turk. The results show that individuals with a higher degree of religiosity are more likely to support censorship of satirical religious cartoons, and though TPE is not positively correlated to support for censorship, respondents’ perception of the effect of the cartoons on themselves may indicate an increased propensity for supporting censorship.     

感性消费时代的广告受众心理分析

通过对广告受众心理进行分析,总结出新形势下广告受众心理的主要特点。具体受众心理分析是：一、感性心理：情趣情调;自我比拟;理想向往;流行时尚;交流互动;品牌偏好。二、理性——感性心理：实用价值观念变异;自我价值中的理性与感性;体现个性。     

中国城乡居民年龄别消费模式量化与分析

文章采用2010年中国家庭追踪调查(CFPS)数据量化分析年龄别居民消费模式。针对同类研究只采用年龄变量作为自变量的不足,在经典的需求和消费函数基础上扩展年龄变量构建计量模型,获得了更好的解释力和准确性。基于计量分析结果刻画了分年龄、性别的中国城乡居民消费模式,并进行城乡比较。研究发现,居民家庭消费生命周期中存在30~34岁及45~49岁两个消费高峰;城乡居民消费差距最大的是高龄老人;农村老人在高龄阶段的医疗保健消费明显下降;农村中年妇女的医疗保健支出低于男性。研究认为,应从政策层面有针对性地加大对特定年龄性别人群的扶助力度;同时,未来产业规划应充分考虑人口结构变动因素的影响。     

Blocking Rumor by Cut

In this paper, a rumor blocking problem is studied with an objective function which is neither submodular or supermodular. We will prove that this problem is NP-hard and give a data-dependent approximation with sandwich method. In addition, we show that every set function has a pair of monotone nondecreasing modular functions as upper bound and lower bound, respectively.     

粤港澳大湾区西部核心产业创新空间提升规划思考 ——以顺德为例

在粤港澳大湾区一体化发展背景下,顺德以土地换产值的传统增长模式难以为继,制造业面临巨大的下行压力,根本原因是城市创新空间系统性、产学研协同创新转化、优质服务的创新空间不能匹配顺德日益增长的发展需求.本文结合产业创新空间相关基础研究,从内外部创新空间格局视角,重点梳理顺德产业创新空间方面的规划探索.从区域创新合作、多元创新协同、提升空间供给等方面总结既有经验并提出新的思考.     

The Importance of Racial Socialization: School‐Based Racial Discrimination and Racial Identity Among African American Adolescent Boys and Girls

This study examined various parental racial socialization messages as mediators between school‐based racial discrimination and racial identity formation over 4 years for African American boys (N = 639) and African American girls (N = 711). Findings indicated that school‐based racial discrimination was associated with racial identity beliefs. For African American boys, behavioral racial socialization messages mediated the relation between school‐based racial discrimination and racial centrality over time. Mediation also resulted for African American girls, but for a different set of race‐related messages (negative messages and racial barriers) and racial identity beliefs. The developmental significance of the findings and implications for future research are discussed.     

A new algorithm to estimate monotone nonparametric link functions and a comparison with parametric approach

The generalized linear model (GLM) is a class of regression models where the means of the response variables and the linear predictors are joined through a link function. Standard GLM assumes the link function is fixed, and one can form more flexible GLM by either estimating the flexible link function from a parametric family of link functions or estimating it nonparametically. In this paper, we propose a new algorithm that uses P-spline for nonparametrically estimating the link function which is guaranteed to be monotone. It is equivalent to fit the generalized single index model with monotonicity constraint. We also conduct extensive simulation studies to compare our nonparametric approach for estimating link function with various parametric approaches, including traditional logit, probit and robit link functions, and two recently developed link functions, the generalized extreme value link and the symmetric power logit link. The simulation study shows that the link function estimated nonparametrically by our proposed algorithm performs well under a wide range of different true link functions and outperforms parametric approaches when they are misspecified. A real data example is used to illustrate the results.     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

An adaptive-to-model test for partially parametric single-index models

Residual marked empirical process-based tests are commonly used in regression models. However, they suffer from data sparseness in high-dimensional space when there are many covariates. This paper has three purposes. First, we suggest a partial dimension reduction adaptive-to-model testing procedure that can be omnibus against general global alternative models although it fully use the dimension reduction structure under the null hypothesis. This feature is because that the procedure can automatically adapt to the null and alternative models, and thus greatly overcomes the dimensionality problem. Second, to achieve the above goal, we propose a ridge-type eigenvalue ratio estimate to automatically determine the number of linear combinations of the covariates under the null and alternative hypotheses. Third, a Monte-Carlo approximation to the sampling null distribution is suggested. Unlike existing bootstrap approximation methods, this gives an approximation as close to the sampling null distribution as possible by fully utilising the dimension reduction model structure under the null model. Simulation studies and real data analysis are then conducted to illustrate the performance of the new test and compare it with existing tests.