社会转型期的师德继承与发展问题的探索

社会转型期使人们的思想方法、价值观念、道德取向都会产生变化,师德建设怎样与社会发展相适应就成为一个值得探索的新问题了,这是因为教育是改造社会的重要力量之一,教师是通过塑造新人来实现对未来社会的改造的。正因为教师这种在整个社会发展、经济建设、文化建设中的重要性及教师职业的特殊性,决定了在社会主义市场经济条件下加强师德建设的重要性。本文对这个古老而常新的问题提出自己的看法     

中心村规划——广州农村可持续发展战略

一种既非农村,又非城市的新的生态群落———被称之为“都市里的村庄”已成为广州城市化进程的严重障碍,因此,城市化进程应该包括改造旧村庄和规划建设新村庄,尤其要重视中心村规划建设。中心村规划,从规划区域上操作,包括“点”与“面”两个方面,即把中心村（点）按城市居住单元（或小区）经济技术指标规划成一个集镇,把中心村管辖范围规划成几个功能区;从规划结构上操作,要完成“六图一书”从规划体系上操作,要在上级区域规划指导下进行     

性爱与情感的灵魂——劳伦斯和他的散论

劳伦斯在文艺思想和创造方法上提出了介入性爱和情感及主观感受真实性的观点。他从这种艺术的观点出发主张要深入到男女的性、爱和血意识中去。本文介绍劳伦斯的《恋爱中的女人》、《查太莱夫人的情人》、《儿子与情人》等小说和《看我们如何漫游》、《不朽的散文选》、《失落的诗章》、《劳伦斯的爱情》等散文中的有关性、爱、情感等的文艺观点     

Composite quantile regression for massive datasets

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Composite quantile regression (CQR) is a robust and efficient estimation method. In this paper, we extend CQR to massive datasets and propose a divide-and-conquer CQR method. The basic idea is to split the entire dataset into several blocks, applying the CQR method for data in each block, and finally combining these regression results via weighted average. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analysed simultaneously. Moreover, to improve the efficiency of CQR, we propose a weighted CQR estimation approach. To achieve sparsity with high-dimensional covariates, we develop a variable selection procedure to select significant parametric components and prove the method possessing the oracle property. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.     

Analysis of dependently truncated data in Cox framework

Truncation is a known feature of bone marrow transplant (BMT) registry data, for which the survival time of a leukemia patient is left truncated by the waiting time to transplant. It was recently noted that a longer waiting time was linked to poorer survival. A straightforward solution is a Cox model on the survival time with the waiting time as both truncation variable and covariate. The Cox model should also include other recognized risk factors as covariates. In this article, we focus on estimating the distribution function of waiting time and the probability of selection under the aforementioned Cox model.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

An additive–multiplicative mean model for panel count data with dependent observation and dropout processes

This paper discusses regression analysis of panel count data with dependent observation and dropout processes. For the problem, a general mean model is presented that can allow both additive and multiplicative effects of covariates on the underlying point process. In addition, the proportional rates model and the accelerated failure time model are employed to describe possible covariate effects on the observation process and the dropout or follow‐up process, respectively. For estimation of regression parameters, some estimating equation‐based procedures are developed and the asymptotic properties of the proposed estimators are established. In addition, a resampling approach is proposed for estimating a covariance matrix of the proposed estimator and a model checking procedure is also provided. Results from an extensive simulation study indicate that the proposed methodology works well for practical situations, and it is applied to a motivating set of real data.     

Rank aggregation using latent-scale distance-based models

Rank aggregation aims at combining rankings of a set of items assigned by a sample of rankers to generate a consensus ranking. A typical solution is to adopt a distance-based approach to minimize the sum of the distances to the observed rankings. However, this simple sum may not be appropriate when the quality of rankers varies. This happens when rankers with different backgrounds may have different cognitive levels of examining the items. In this paper, we develop a new distance-based model by allowing different weights for different rankers. Under this model, the weight associated with a ranker is used to measure his/her cognitive level of ranking of the items, and these weights are unobserved and exponentially distributed. Maximum likelihood method is used for model estimation. Extensions to the cases of incomplete rankings and mixture modeling are also discussed. Empirical applications demonstrate that the proposed model produces better rank aggregation than those generated by Borda and the unweighted distance-based models.

     

基于fused惩罚的稀疏主成分分析

本文旨在研究基于fused惩罚的稀疏主成分分析方法,以适用于相邻变量之间高度相关甚至完全相等的数据情形。首先,从回归分析角度出发,提出一种求解稀疏主成分的简便思路,给出一种广义的稀疏主成分模型—— GSPCA模型及其求解算法,并证明在惩罚函数取1-范数时,该模型与现有的稀疏主成分模型——SPC模型的求解结果一致。其次,本文提出将fused惩罚与主成分分析相结合,得到一种fused稀疏主成分分析方法,并从惩罚性矩阵分解和回归分析两个角度,给出两种模型形式。在理论上证明了两种模型的求解结果是一致的,故将其统称为FSPCA模型。模拟实验显示,FSPCA模型在处理相邻变量之间高度相关甚至完全相等的数据集上的表现良好。最后,将FSPCA模型应用于手写数字识别,发现与SPC模型相比,FSPCA模型所提取的主成分具备更好的解释性,这使得该模型更具实用价值。     

出口产品质量与技能溢价：理论机制及中国证据

本文基于出口产品“质”的视角,理论阐释并实证检验出口产品质量对技能溢价的影响及其作用机制。研究发现：出口产品质量提高显著提升了劳动力技能溢价,这种提升效应在高技术行业更为明显;出口产品质量升级主要通过增加对高技能劳动力相对需求、改善企业利润和绩效等机制提升技能溢价,但出口产品质量影响技能溢价的产品价格机制未得到验证;技能溢价与高技能劳动力供给之间呈倒“U”型关系。因此,为防止出口产品质量升级加剧技能工资不平等,需要采取加强人力资本投资、增加高素质劳动力供给、完善与高质量外贸发展相适应的相对公平的收入分配政策等措施。