地方政府竞争下的产业选择、就业和收入分配

文章构建了基于地方政府竞争的数量模型。研究表明:在一系列的激励下,中国的地方政府为经济增长而展开了激烈竞争。由于资本密集型产业相对于劳动力密集型产业来说,具有更高的生产效率和更强的增长效应,因此,地方政府在引进和选择产业发展时,会倾向于优先发展资本密集型产业。为此,他们会采取多种措施来吸引、扶持资本密集型产业的发展,忽视劳动密集型产业。而资本密集型产业的过度发展,一是会造成机器和技术对劳动力的替代,使得中国出现"低就业的增长";二是造成资本在收入中的谈判能力不断提高,而劳动力的谈判能力不断降低,从而使得国民收入的初次分配中资本的占比不断提高,而劳动的占比则不断降低。     

“两型”视角下区域产业选择实证研究

"两型"视角下,区域发展应如何选择产业,对其经济增长、资源节约和环境保护起着至关重要的作用。文章首先从产业"两型"水平和产业发展能力两个维度构建"两型"视角下产业选择矩阵,并据此将该矩阵划分为四个象限。然后以重庆市38个工业产业为研究对象,利用主成分分析法对其"两型"水平和发展能力进行测算,并根据测算结果将这些产业划分到产业选择矩阵的四个象限。在此基础上,根据不同象限产业的特点对重庆市"两型"视角下产业发展提出相应建议。     

小产权房开发与交易管制的路径选择

合理管制小产权房开发与交易,要改农村土地承包制为永佃制,赋予乡居民以"恒产",建立健全集体建设土地交易与开发利用机制;严格控制和规范政府征地,允许乡居民依法参与开发经营城镇建设用地范围外的非公益性项目用地;重新建构城乡居民住房开发与供给模式,将符合规范的小产权房纳入合法的住房开发与供应体系;改户籍制为居住制,允许城乡居民自由选择居住地和住所,消除小产权房购买者身份藩篱。     

农村大龄青年的择偶压力及社会风险——基于华北九个村落的实地研究

伴随出生性别比失衡及择偶场域中不平等婚姻策略的消极影响,农村大龄青年目前面临较大的择偶困难与压力。高额且无力承受的婚姻支付、相亲的择偶年龄限制、多类别且高难度的择偶困境,构成大龄青年择偶的主要压力。大龄青年长期未婚,给农村社会带来诸多潜在与显在的社会风险,导致青年个人及父母精神压力的增强、家庭成员间矛盾的凸显、非理性观念的蔓延和失范行为的增多等问题的产生。缓解择偶压力与防控社会风险,需要从挖掘择偶压力与社会风险产生的源头切入,多力量参与、协同治理。     

《河岳英灵集》选评李颀诗及其诗歌史意义

《河岳英灵集》遴选李颀诗十四首,序列上卷第八位。以品评观之,殷璠在选本中重点论析李颀的诗歌内容、艺术成就。同时,又摘句品评、叹惋李颀生平的不幸,兼与其他诗人比较。以遴选诗篇观之,其内容包括寄赠送答诗什、怀人伤时诗章等,突出了李颀诗“发调既清”“玄理最长”等特点。以遴选诗什体裁观之,选本体现了李颀以古体为主的特点。殷璠选评李颀诗,其不仅具有存录诗章意义,亦有考订篇目、资证辨疑价值。最值得关注的是其文学史意义,不仅体现在对李颀诗内容特征的认识上,也体现在李颀诗艺术价值的发掘层面。此外,殷璠选评李颀诗,亦具有接受批评史、文化史等多方面的意义。     

Readouts for echo-state networks built using locally regularized orthogonal forward regression

Echo state network (ESN) is viewed as a temporal expansion which naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echo-regressors effectively explain the teacher output and we propose to determine the importance of the echo-regressors by a joint calculation of the individual variance contributions and Bayesian relevance using the locally regularized orthogonal forward regression (LROFR). This information can be advantageously used in a variety of ways for an analysis of an ESN structure. We present a locally regularized linear readout built using LROFR. The readout may have a smaller dimensionality than the ESN model itself, and improves robustness and accuracy of an ESN. Its main advantage is ability to determine what type of an additional readout is suitable for a task at hand. Comparison with PCA is provided too. We also propose a radial basis function (RBF) readout built using LROFR, since flexibility of the linear readout has limitations and might be insufficient for complex tasks. Its excellent generalization abilities make it a viable alternative to feed-forward neural networks or relevance-vector-machines. For cases where more temporal capacity is required we propose well studied delay&sum readout.     

A novel bagging approach for variable ranking and selection via a mixed importance measure

At present, ensemble learning has exhibited its great power in stabilizing and enhancing the performance of some traditional variable selection methods such as lasso and genetic algorithm. In this paper, a novel bagging ensemble method called BSSW is developed to implement variable ranking and selection in linear regression models. Its main idea is to execute stepwise search algorithm on multiple bootstrap samples. In each trial, a mixed importance measure is assigned to each variable according to the order that it is selected into final model as well as the improvement of model fitting resulted from its inclusion. Based on the importance measure averaged across some bootstrapping trials, all candidate variables are ranked and then decided to be important or not. To extend the scope of application, BSSW is extended to the situation of generalized linear models. Experiments carried out with some simulated and real data indicate that BSSW achieves better performance in most studied cases when compared with several other existing methods.     

Adaptive LASSO for linear mixed model selection via profile log-likelihood

Mixed model selection is quite important in statistical literature. To assist the mixed model selection, we employ the adaptive LASSO penalized term to propose a two-stage selection procedure for the purpose of choosing both the random and fixed effects. In the first stage, we utilize the penalized restricted profile log-likelihood to choose the random effects; in the second stage, after the random effects are determined, we apply the penalized profile log-likelihood to select the fixed effects. In each stage, the Newton–Raphson algorithm is performed to complete the parameter estimation. We prove that the proposed procedure is consistent and possesses the oracle properties. The simulations and a real data application are conducted for demonstrating the effectiveness of the proposed selection procedure.     

Robust image segmentation via Bayesian type criterion

Image segmentation plays an important role in image processing before image recognition or compression. Many segmentation solutions follow the information theoretic criteria and often have excellent results; however, they are not robust to reduce the noise effect in contaminated image data. To guarantee the optimal segmentation with possible noise, a robust Bayesian information criterion is proposed to segment a grayscale image and it is less sensitive to noise. The asymptotic properties are also studied. Monte Carlo numerical experiments along with a brain magnetic resonance image are conducted to evaluate the performance of the new method.