Structural identification and variable selection in high-dimensional varying-coefficient models

Varying-coefficient models have been widely used to investigate the possible time-dependent effects of covariates when the response variable comes from normal distribution. Much progress has been made for inference and variable selection in the framework of such models. However, the identification of model structure, that is how to identify which covariates have time-varying effects and which have fixed effects, remains a challenging and unsolved problem especially when the dimension of covariates is much larger than the sample size. In this article, we consider the structural identification and variable selection problems in varying-coefficient models for high-dimensional data. Using a modified basis expansion approach and group variable selection methods, we propose a unified procedure to simultaneously identify the model structure, select important variables and estimate the coefficient curves. The unique feature of the proposed approach is that we do not have to specify the model structure in advance, therefore, it is more realistic and appropriate for real data analysis. Asymptotic properties of the proposed estimators have been derived under regular conditions. Furthermore, we evaluate the finite sample performance of the proposed methods with Monte Carlo simulation studies and a real data analysis.     

Automatic structure discovery for varying-coefficient partially linear models

Varying-coefficient partially linear models provide a useful tools for modeling of covariate effects on the response variable in regression. One key question in varying-coefficient partially linear models is the choice of model structure, that is, how to decide which covariates have linear effect and which have non linear effect. In this article, we propose a profile method for identifying the covariates with linear effect or non linear effect. Our proposed method is a penalized regression approach based on group minimax concave penalty. Under suitable conditions, we show that the proposed method can correctly determine which covariates have a linear effect and which do not with high probability. The convergence rate of the linear estimator is established as well as the asymptotical normality. The performance of the proposed method is evaluated through a simulation study which supports our theoretical results.     

会计委派制存在的问题及对策

具体分析了会计委派制的实用价值,并结合其存在的弊端,即缺乏法律依据、与现代企业制度相背离以及不利于充分发挥会计的管理职能,进行了会计委派制的可行性研究,并针对会计委派制的法律依据、职责和权限、实施范围和委派方式等方面提出了完善建议,为加强财务会计管理,进一步探讨和完善会计委派制的理论内容明确了思路。     

马克思主义教育创新的时代坐标

从高等教育培育人才、传承文明和服务社会的三维功能结构出发,论析了马克思主义教育创新的时代坐标,进而指出培育创新人才是马克思主义教育创新的主体定位,高扬时代精神是马克思主义教育创新的文化定位,关注社会生活是马克思主义教育创新的实践定位。培育创新人才、高扬时代精神和关注社会生活,构成了马克思主义教育创新的三维坐标,也展示了全面推进马克思主义教育创新的三大维度。     

山东省高中段教育结构调整与发展研究

高中阶段教育是整个教育结构的中间层次 ,处于承上启下的地位 ,合理确定高中段教育的发展规模和速度以及内部各类教育之间的结构配比 ,是使其协调发展的关键。综合高中 ,是高中段教育的新的办学模式。     

论楹联"流水对"

一种有影响的观点认为,流水对就是复合句.这种观点缩小了流水对的范围,因为流水对不限于复合句;这种观点又扩大了流水对在复合句里的范围,因为并非所有复合句形式的对联都构成流水对.     

对完善中国劳动法制若干问题的探讨

我国劳动法制正面临许多挑战。为实现构建和谐社会理念，我国的劳动法制需要在理论、方法和各项基本制度方面尝试作出完善和改革。文章在劳动法适用范围制度、协调机制和劳动标准等方面提出了若干设想和建议。     

试论地方行政体制改革中三大关系的统筹问题--在三维空间中确定地方行政体制改革的新坐标

深化地方行政体制改革,必须统筹思考纵向的中央与地方、横向的政府与社会以及内向的政府角色二重化三个关系.对这三大关系各个侧面的统筹分析,在三维的空间中定位出了地方行政体制改革的新坐标--在政府与社会的上下齐动下,建立起中央集权但不专权,地方分权但不分散,以民为本、法制完备,政府、社会、市场职能各有所归,执行高效,手段先进,强力监督,自我约束的有限与服务型小政府.     

三维空间中的Dirichlet自由变形算法

自由变形是与物体表示无关的变形方法的重要分支,被广泛地应用于计算机动画和几何建模领域中。Dirichlet自由变形是众多自由变形方法中的一种,相比其他的自由变形方法,它有更大的灵活性,能够任意设置控制点。Dirichlet自由变形算法在二维空间已获得了广泛的应用,但在三维空间中的应用却非常少。该文研究Dirichlet自由变形算法在三维空间的实现问题,将Dirichlet自由变形算法应用到三维空间中的头像变形中,获得了较好的变形效果。     

钢琴伴奏的艺术探索

钢琴伴奏是表演艺术的一种形式,它是通过合作来体现其价值的。钢琴伴奏与合作者是一个整体,缺一不可,因此在表演艺术中有着极其重要的作用。