Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

Modal non‐linear regression in the presence of Laplace measurement error

In this paper, we propose a robust estimation procedure for a class of non‐linear regression models when the covariates are contaminated with Laplace measurement error, aiming at constructing an estimation procedure for the regression parameters which are less affected by the possible outliers, and heavy‐tailed underlying distribution, as well as reducing the bias introduced by the measurement error. Starting with the modal regression procedure developed for the measurement error‐free case, a non‐trivial modification is made so that the modified version can effectively correct the potential bias caused by measurement error. Large sample properties of the proposed estimate, such as the convergence rate and the asymptotic normality, are thoroughly investigated. A simulation study and real data application are conducted to illustrate the satisfying finite sample performance of the proposed estimation procedure.     

一种应用于药品泡罩包装检测的改进分水岭算法研究

针对分水岭算法存在的过分割问题以及泡罩包装药品图像的特点,提出一种改进的分水岭分割算法对泡罩包装的药品进行分割。该方法将形态学重建运算与使用控制标记符的方法相结合,首先对原图像进行开闭重建,根据邻域像素连通性提取重建后图像的局部极大值作为标记,从而抑制其他的局部极小值,然后依据标记对原始的梯度图像进行修正,最后对修改后的梯度图像进行分水岭分割。试验表明,该方法在缺陷药品检测中,可以有效的抑制传统分水岭的过分割,分割效果良好。     

资产专用性对确权后农地流转的影响

农地确权后,农户对农地转出多持中立或否定的态度,这与产权明晰促进交易的产权理论大相径庭。文章探讨了资产专用性对确权后农地流转的作用机理,并运用吉林省9市1444个农户数据对其进行实证分析。结果表明：农地确权后,土地资产、农业资产、地理位置以及人力资本等专用性抑制农户转出农地,农业资产和人力资本专用性抑制农户提高农地租金,而社会资本专用性则分别存在促进作用;农业资产、地理位置以及人力资本等专用性抑制农户更长时间转出农地;农业资产专用性对转出农地须要签订合约具有抑制作用,而地理位置专用性则存在促进作用。     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

女性欲望的分裂与张扬--对张爱玲《心经》中女性形象的再认识

旨在探讨《心经》中独特的女性形象以及张爱玲写此文的深层原因。生活在父权制社会下,男性凝望世界中的女性,渴望直接与男性对话,从而产生异样感情,特殊的母女关系经历了分裂到张扬再到重组的过程,表现出女性在与男性相抗衡的过程中的绝望与疯狂,也隐含作者内心深处的希望。     

高校课程育人：本真、理路与评价

立德树人居于学校教育发展全局的核心位置,需要将其落实到课程育人的生动实践中。但是,在课程育人的具体实践中,仍有部分教师将其简单化为“课程+育人”的生硬结合,导致课程育人容易陷入理论教条窠臼。为此,高校教师应充分认识到高校课程育人在目标要求、重点内容、转型路径上的人性化、时代化和数字化本真意蕴,从教师素养、课程建设、课堂教学三个方面开展育人实践。在此基础上,针对具体课程的育人效果而言,可以从目标设定、内容选择、方法实施、结果呈现等方面进行评价,从而进一步推动课程育人的生动实践。     

普通高校公共体育教学的改革实验与结果分析

从体育课程设置、教学内容、教学组织形式、学生体育考核等方面对“三阶段式”体育教学改革进行分析,可以发现,“三阶段式”体育教学具有实效性,符合现阶段学生实际。由此也发现,体育教学方法、手段只有尽可能体现快乐健康教育的特点,才能取得好的教学效果。     

劳动力转移的困境和挑战——基于对凉山彝族外出务工人员困境的思考

目前,在凉山彝区,外出务工是劳动力转移的主要方式,许多家庭通过外出务工脱贫致富。在当地无力承担和消化剩余劳动力的情况下,外出务工的现象将长期存在,正确认识彝区劳动力转移的合理性非常必要。为此笔者对四川凉山的冕宁、盐源等地彝区外出务工人员较多的村寨通过田野调查方式,了解当地劳动力转移的历史和现状,掌握存在的问题并分析其原因,提出解决思路和具体措施。应该建立劳动力转移的社会支持系统,继续不懈地积极推进劳动力转移工作。继续加大各级职业技术教育,增强劳动力转移的市场竞争力。加强法制教育,促进用人机构和彝族群众增强法制意识,依法促进彝族务工者的城市融入。进一步强化乡镇劳务开发工作体系建设,加强跨区域合作,倡导有序输出,规范对劳务经纪人的管理,强化劳务培训质量,提高彝族务工人员素质,保护彝族务工人员的合法益。     

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.