Robust modal estimation and variable selection for single-index varying-coefficient models

In this article, we present a new efficient iteration estimation approach based on local modal regression for single-index varying-coefficient models. The resulted estimators are shown to be robust with regardless of outliers and error distributions. The asymptotic properties of the estimators are established under some regularity conditions and a practical modified EM algorithm is proposed for the new method. Moreover, to achieve sparse estimator when there exists irrelevant variables in the index parameters, a variable selection procedure based on SCAD penalty is developed to select significant parametric covariates and the well-known oracle properties are also derived. Finally, some numerical examples with various distributed errors and a real data analysis are conducted to illustrate the validity and feasibility of our proposed method.     

Variable selection in partially linear additive models for modal regression

Based on B-spline basis functions and smoothly clipped absolute deviation (SCAD) penalty, we present a new estimation and variable selection procedure based on modal regression for partially linear additive models. The outstanding merit of the new method is that it is robust against outliers or heavy-tail error distributions and performs no worse than the least-square-based estimation for normal error case. The main difference is that the standard quadratic loss is replaced by a kernel function depending on a bandwidth that can be automatically selected based on the observed data. With appropriate selection of the regularization parameters, the new method possesses the consistency in variable selection and oracle property in estimation. Finally, both simulation study and real data analysis are performed to examine the performance of our approach.     

Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.     

Parameter estimation of the censored δ-shock model on uniform interval

The censored δ-shock model is a special kind of shock model and it has very important research values in the reliability theory. In this paper, we discuss the parameter estimation of the censored δ-shock model when the inter-arrival times between two successive shocks follows uniform distribution on [a, b]. With the maximum likelihood estimation, we obtain the parameter estimator and expectation of estimator and lifetime of the model. By numerical simulation, we get the empirical result on the estimator of δ and the relationship between δ and other parameters.     

Complete moment convergence of widely orthant dependent random variables

It is known that the dependence structure of widely orthant dependent (WOD) random variables is weaker than those of negatively associated (NA) random variables, negatively superadditive dependent (NSD) random variables, negatively orthant dependent (NOD) random variables, and extended negatively dependent (END) random variables. In this article, the results of complete moment convergence and complete convergence are presented for WOD sequence under the same moment conditions as independent sequence in classical result (Chow 1988 Chow, Y. (1988). On the rate of moment convergence of sample sums and extremes. Bull. Inst. Math. Acad. Sin. 16(3):177–201. [Google Scholar]).     

城中村农民社会研究的人类学维度与限度

从人类学的角度来看,城中村的农民、农业、农村社会变迁和包括少数民族在内的大量外来务工人员的文化移植或嵌入,共同构筑了城中村农民社会的文化多样性.人类学独特的理论视角和方法技术,可避免其他学科可能出现的“文化解释”谬误.文章分析了社会情境影响下的中国人类学发展状况和人类学参与城中村农民社会研究的学科优势,从都市人类学、工商人类学、经济人类学、教育人类学、医学人类学、宗教人类学、历史人类学、旅游人类学等8个分支学科讨论了人类学的研究维度,也讨论了人类学参与研究中存在超域性、局部性和相对性限度,揭示了人类学参与城中村农民社会研究的重要意义.     

论原生态民族舞蹈的艺术化及其保护

普通民众的社会生活是一切舞蹈艺术的源泉,原生态民族舞蹈的生成和发展离不开自然环境和社会因素.贴近时代脉搏,不断向艺术化舞蹈迈进是支撑原生态民族舞蹈发展的动力所在.原生态的民族舞蹈向艺术化的舞蹈演变是大势所趋,而艺术化舞蹈的形成是以原生态舞蹈为基础的.本文就原生态民族舞蹈到艺术化舞蹈的演变以及对它们的保护对策展开论述.     

经济学方法论的个体主义、集体主义及其超越

方法论个体主义和方法论集体主义的分歧源于社会理论中的能动性与结构问题。方法论个体主义体现了一个极端,即强调行为主体而不是社会结构,是一种试图仅用个体相关的方式处理社会结构、制度和其他总体现象的方式。方法论集体主义体现了另一个极端,强调整体结构而不是个体,认为所有个体倾向或行为能够也应当从社会的、结构的、文化的或者制度的现象中得到解释。这两个极端都有其缺陷:个体主义否定结构影响,不承认多层次系统中存在着不同的"质"层级,集体主义对改变个体的过程和机制也未给予充分的关注。而对这两者的超越与综合则包括了结构化理论、批判实在论、凡勃伦制度主义、奥地利传统的互动主义以及基于现代系统论的系统主义等。结构化理论和批判实在论试图从实体层次上分离能动性与结构,凡勃伦制度主义和互动主义试图从不同的角度解释能动性与结构之间的双向因果关系,系统主义则采用系统论的思考方式,强调不同层级和不同网络中的循环因果关系。