Generalized Augmentation to Control the False Discovery Exceedance in Multiple Testing

Abstract.  A new multiple testing procedure, the generalized augmentation procedure (GAUGE), is introduced. The procedure is shown to control the false discovery exceedance and to be competitive in terms of power. It is also shown how to apply the idea of GAUGE to achieve control of other error measures. Extensions to dependence are discussed, together with a modification valid under arbitrary dependence. We present an application to an original study on prostate cancer and on a benchmark data set on colon cancer.     

Fast robust feature screening for ultrahigh-dimensional varying coefficient models

In this paper, we propose a new partial correlation, the so-called composite quantile partial correlation, to measure the relationship of two variables given other variables. We further use this correlation to screen variables in ultrahigh-dimensional varying coefficient models. Our proposed method is fast and robust against outliers and can be efficiently employed in both single index variable and multiple index variable varying coefficient models. Numerical results indicate the preference of our proposed method.     

Semi-parametric segmentation of multiple series using a DP-Lasso strategy

We consider a semi-parametric approach to perform the joint segmentation of multiple series sharing a common functional part. We propose an iterative procedure based on Dynamic Programming for the segmentation part and Lasso estimators for the functional part. Our Lasso procedure, based on the dictionary approach, allows us to both estimate smooth functions and functions with local irregularity, which permits more flexibility than previous proposed methods. This yields to a better estimation of the functional part and improvements in the segmentation. The performance of our method is assessed using simulated data and real data from agriculture and geodetic studies. Our estimation procedure results to be a reliable tool to detect changes and to obtain an interpretable estimation of the functional part of the model in terms of known functions.     

Joint GEEs for multivariate correlated data with incomplete binary outcomes

This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.     

Vine copula constructions of higher-dimensional dependent reliability systems

Copulas have proved to be very successful tools for the flexible modeling of dependence. Bivariate copulas have been deeply researched in recent years, while building higher-dimensional copulas is still recognized to be a difficult task. In this paper, we study the higher-dimensional dependent reliability systems using a type of decomposition called “vine,” by which a multivariate distribution can be decomposed into a cascade of bivariate copulas. Some equations of system reliability for parallel, series, and k-out-of-n systems are obtained and then decomposed based on C-vine and D-vine copulas. Finally, a shutdown system is considered to illustrate the results obtained in the paper.     

Decomposition of the main effects and interaction term by using orthogonal polynomials in multiple non symmetrical correspondence analysis

The multiple non symmetric correspondence analysis (MNSCA) is a useful technique for analyzing a two-way contingency table. In more complex cases, the predictor variables are more than one. In this paper, the MNSCA, along with the decomposition of the Gray–Williams Tau index, in main effects and interaction term, is used to analyze a contingency table with two predictor categorical variables and an ordinal response variable. The Multiple-Tau index is a measure of association that contains both main effects and interaction term. The main effects represent the change in the response variables due to the change in the level/categories of the predictor variables, considering the effects of their addition, while the interaction effect represents the combined effect of predictor categorical variables on the ordinal response variable. Moreover, for ordinal scale variables, we propose a further decomposition in order to check the existence of power components by using Emerson's orthogonal polynomials.     

论汉代政权对民间信仰的多重政策

民间信仰是中国传统文化的重要组成部分,也是俗文化的核心。针对民间自发形成的信仰与崇拜,汉代政权采取多重政策,或引导支持,或借鉴利用,或排斥打击,主要取决于其是否与官方信仰及主流文化相抵触,是否从根本上影响到社会秩序和百姓生产生活。不管官方态度如何,作为一种强大的民间力量,民间信仰以其特有的方式参与政治生活,与官方政权相互影响、相互补充,形成既配合又抗衡的互动局面,并以顽强的生命力传承、延续和发展。     

高校经管类学生参与科研意愿的影响因素分析——基于安徽省8所高校的问卷调查

大学生参与科研是高校培养双创型人才的重要途径之一,高校经管类专业学生培养存在重理论、轻实践的现象。安徽省8所高校1406名经济管理类学生科研意愿的实证分析表明:年级、学习成绩、科研重要性认知、科研了解程度和学校科研活动开展对大学生科研意愿有显著影响。高校在经管类学生培养过程中应通过加强科研宣传推广、开设科研训练课程、丰富科研活动多样性、激励教师科研指导和开拓校外实践教学基地建设等措施增强学生参与科研的积极性,提升学生的双创能力。     

跨国企业集团内部财务控制博弈研究

由于跨国企业集团的管理与控制结构的层级较多,其内部母子公司的财务控制机制由于地域限制和文化差异存在较大的失效风险。本文通过构建跨国集团母公司、子公司、基层员工间的三方动态博弈模型,分析了跨国集团在跨地域内部财务控制机制构建中需要重视的主要影响因素,分析各个真正可控参数的作用机理并列出了跨国集团可行的控制方法的优劣排序。     

论道德教育的多学科生态

道德教育是一个完整的、有机的形态,具有其生态本性。文章从此逻辑起点出发,提出道德教育研究本身具有其生态性．拘泥于某一特定学科视角局限的道德教育研究缺乏全局性的总体把握。因此,从道德教育的生态整体性出发,从伦理学、教育学、社会学、心理学等多学科视角,从支持系统、操作系统、环境系统和转化系统建构方面,全面分析道德教育,建构起多学科研究视野中的道德教育生态体系,是解决当前道德危机和德育困境的重要途径。