An elastic-net penalized expectile regression with applications

To perform variable selection in expectile regression, we introduce the elastic-net penalty into expectile regression and propose an elastic-net penalized expectile regression (ER-EN) model. We then adopt the semismooth Newton coordinate descent (SNCD) algorithm to solve the proposed ER-EN model in high-dimensional settings. The advantages of ER-EN model are illustrated via extensive Monte Carlo simulations. The numerical results show that the ER-EN model outperforms the elastic-net penalized least squares regression (LSR-EN), the elastic-net penalized Huber regression (HR-EN), the elastic-net penalized quantile regression (QR-EN) and conventional expectile regression (ER) in terms of variable selection and predictive ability, especially for asymmetric distributions. We also apply the ER-EN model to two real-world applications: relative location of CT slices on the axial axis and metabolism of tacrolimus (Tac) drug. Empirical results also demonstrate the superiority of the ER-EN model.     

时光·梦想

生活在19世纪的人们,乐于听到工业革命的轰鸣声。他们知道,这是“现代”的召唤。21世纪的我们,现代的我们则渴望着回到自然的怀抱。这是文明的又一道曲线。它从一个欣欣向荣滑到另一个欣欣向荣。而这两个时代都打上了进步的鲜明烙印,都同样是我们光荣的、关于自身、关于梦想的珍贵记忆。如果好奇于日常生活中的周遭事务,并执着于探求它们的来源,我们会惊奇地发现:时常光顾的超市、购物中心、度假村、俱乐部,始于世界博览会;电视机、留声机、电灯,在世博会上首次展出;甚至第一台纺织机、第一架飞机竟也是从世博会步入我们的生活。“一切始于…     

A primal dual active set with continuation algorithm for high-dimensional nonconvex SICA-penalized regression

The smooth integration of counting and absolute deviation (SICA) penalty has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection and parameter estimation. However, solving the nonconvex optimization problem associated with SICA penalty in high-dimensional setting remains to be enriched, mainly due to the singularity at the origin and the nonconvexity of the SICA penalty function. In this paper, we develop a fast primal dual active set (PDAS) with continuation algorithm for solving the nonconvex SICA-penalized least squares in high dimensions. Upon introducing the dual variable, the PDAS algorithm iteratively identify and update the active set in the optimization using both the primal and dual information, and then solve a low-dimensional least square problem on the active set. When combined with a continuation strategy and a high-dimensional Bayesian information criterion (BIC) selector on the tuning parameters, the proposed algorithm is very efficient and accurate. Extensive simulation studies and analysis of a high-dimensional microarray gene expression data are presented to illustrate the performance of the proposed method.     

A comparative study of confidence intervals to assess biosimilarity from analytical data

Assessment of analytical similarity of tier 1 quality attributes is based on a set of hypotheses that tests the mean difference of reference and test products against a margin adjusted for standard deviation of the reference product. Thus, proper assessment of the biosimilarity hypothesis requires statistical tests that account for the uncertainty associated with the estimations of the mean differences and the standard deviation of the reference product. Recently, a linear reformulation of the biosimilarity hypothesis has been proposed, which facilitates development and implementation of statistical tests. These statistical tests account for the uncertainty in the estimation process of all the unknown parameters. In this paper, we survey methods for constructing confidence intervals for testing the linearized reformulation of the biosimilarity hypothesis and also compare the performance of the methods. We discuss test procedures using confidence intervals to make possible comparison among recently developed methods as well as other previously developed methods that have not been applied for demonstrating analytical similarity. A computer simulation study was conducted to compare the performance of the methods based on the ability to maintain the test size and power, as well as computational complexity. We demonstrate the methods using two example applications. At the end, we make recommendations concerning the use of the methods.     

陈寅恪的“旧巢痕”

《寒柳堂记梦未定稿》可谓是中国现代文学中一篇至痛至哀之文 ,而陈寅恪寄寓其中的历史之感与家国之情均由“旧巢痕”而起 ,这便在一定程度上构成了精神分析学意义上的创作“心理原型”。因而 ,对此“旧巢痕”意象加以详细审视和追究 ,似可找到陈氏之诗的根源所在。陈氏之“旧巢痕”一语 ,大致有远、近二源。其远源始于北宋著名诗人苏轼 ,其近源则见诸乃父散原先生 190 1年所作《书感》一诗。而其深层含义 ,则宜解读为那个直接生产了中国旧日世家的传统社会结构和文化制度之彻底崩溃。     

政府俘获理论的比较分析——芝加哥学派与弗吉尼亚学派

芝加哥学派和弗吉尼亚学派是公共选择理论的两个重要学派,它们都从经济学角度对规制领域中的政府俘获问题(或利益集团问题)进行了深入研究,提出了规制机构为特殊利益集团服务的观点。但是,二者在理论假设、研究内容、研究方法、竞争影响、租金耗散等问题上观点迥异,从而形成20世纪七八十年代政府俘获理论研究上的不同派别。     

“场域-惯习”视角下的水电移民长期补偿安置方式

借鉴布迪厄的场域-惯习理论,从移民安置的场域、资本、惯习及其相互之间的作用出发,构建水电移民安置方式分析框架。应用该分析框架对水电移民长期补偿安置方式实施的原因、存在的风险和可能的变化进行社会学解读。     

新制度经济学视角下的社会主义新农村建设

建设新农村要打破小农社会的内在稳定,必须引入新的要素。新制度经济学认为要引入的最重要的要素是制度。但制度的学习引入需要非正式制度发生相应变化,由此新农村建设的关键是改变非正式制度,特别是农民的不利于发展的观念与小农社会的规范与习俗。为此,必须加大对社会文明基础设施投入的转移支付的力度,以促进信息、人口流动,改变农民所处的环境,从而促使农村非正式制度的变化。     

政府主导工业化:经济发展的“中国模式”

发展经济学理论认为经济发展存在着阶段性,不同经济发展阶段存在不同的技术和组织特征;但它没回答促使经济发展技术和组织特征变革的内在动力是什么,许多国家在不同发展阶段遇到了“发展陷阱”.本文通过对新中国60年经济发展经验进行总结认为,经济发展存在动态阶段性,经济体系没有内在的冲破阶段阻碍的力量,政府作为一种重要的外部发展机制可以通过内在制度安排和协调缩短寻找转型的痛苦时期,避免经济陷入“发展陷阱”.对中国经济发展60年历史经验的分析,补充了现有经济理论关于政府作用的论述.政府主导工业化、主导超越经济发展阶段性的机制是中国经济发展对经济发展理论的最大贡献,因此本文认为构建经济发展的“中国模式”应以政府主导工业化为核心.     

修德而后可讲学——论《明儒学案》的精神

长期以来,学界主要从哲学、学术史、体例学等方面来研究《明儒学案》,很少有人从修德角度来探讨《明儒学案》。其实黄宗羲的创作宗旨在于修德,《明儒学案》的精神是“修德而后可讲学”。围绕着修德,黄宗羲从三方面来评论明代学术:一是修德重在自得,学术要在自家身心上生根。二是修德深浅关乎学术深浅。判断学术深浅要考察其圆融性和深刻性。圆融性要求说话前后一贯,理论没有矛盾。深刻性可以从如何解决本体与工夫,从如何回答理-心凑泊、儒-释分别这两个问题,从如何进行学术辩论,从如何讨论同一个话题这四方面看出。三是修德具有效果,可以使人坦然面对贫穷、危险、死亡,可以“所过者化”。本文通过考察黄宗羲对明儒学术的评论,再现了黄宗羲修德的深浅,建构了黄宗羲的修德理论,同时期望引起现代学者能对自己的学术研究进行深刻反思。