与时俱进——党的思想路线的新发展

与时俱进和解放思想、实事求是统一起来,丰富发展了党的思想路线.解放思想、实事求是,是与时俱进的前提与基础,而与时俱进则是检验解放思想、实事求是效果与程度的标准.三者辩证统一于社会主义现代化建设的实践之中.坚持与时俱进,就是党的全部理论探索与实际工作都要体现时代性、把握规律性、富于创造性.     

转机并非转折——中国经济摆脱“疲软”的制度经济学分析

本文认为 ,我国处于市场化改革的进程中 ,既不存在比较发达和完善的市场制度 ,也不存在严重的市场失效 ,市场机制难以有效发挥作用 ,因此采用西方凯恩斯主义和货币主义的措施难以启动经济 ,应利用新制度经济学方法研究我国现实问题。文章指出 ,我国在制度上的主要矛盾是改革变形 ,改革变形的深层原因在于中国经济自身有不少不健康因素 ,对此应有清醒的理性认识     

论马克思最低工资理论的形成与发展

最低工资理论是马克思工资学说的重要组成部分。马克思最低工资理论经历了产生、发展、成熟和完善等几个发展阶段。马克思对最低工资的内容，它与劳动力价值的关系以及与此相联系的工人阶级经济斗争的意义都经历了一个从初步的认识到完整的科学认识的发展过程     

职工养老保险社会统筹制度计发方式研究

中国城镇职工养老保险制度的改革取得了重大成就,为退休人员提供了基本生活保障。但是随着改革的不断深化,其社会统筹制度计发方式中存在的问题不断显现且影响日益加深。本文提出现有计发方式中存在的5个显著问题,包括确定计发工资基数的年限量度过短、最低缴费年限过低、缴费年限工资比重同一化、计发工资与缴费工资差异显著和养老金待遇调整机制不合理。本文在借鉴国际经验的基础上,对优化计发方式、实现社会统筹制度可持续发展提出了政策建议。     

A general algorithm for computing simultaneous prediction intervals for the (log)-location-scale family of distributions

Making predictions of future realized values of random variables based on currently available data is a frequent task in statistical applications. In some applications, the interest is to obtain a two-sided simultaneous prediction interval (SPI) to contain at least k out of m future observations with a certain confidence level based on n previous observations from the same distribution. A closely related problem is to obtain a one-sided upper (or lower) simultaneous prediction bound (SPB) to exceed (or be exceeded) by at least k out of m future observations. In this paper, we provide a general approach for computing SPIs and SPBs based on data from a particular member of the (log)-location-scale family of distributions with complete or right censored data. The proposed simulation-based procedure can provide exact coverage probability for complete and Type II censored data. For Type I censored data, our simulation results show that our procedure provides satisfactory results in small samples. We use three applications to illustrate the proposed simultaneous prediction intervals and bounds.     

D-1-Partially efficiency-balance designs with at most two efficiency classes

A wide class of block designs admitting a simple analysis has been considered. The statistical properties of such designs have been indicated and the problems relating to their characterization and construction have been investigated.     

基于自忆性原理的多变量MGM(1,m)耦合系统模型构建及应用

针对小样本条件下具有相互制约关系的多变量系统,本文提出了一种新颖的多变量MGM(1,m)自忆性耦合系统模型,用来统一描述系统各变量间关系并且提高其建模精度。该模型通过有机耦合动力系统自忆性原理与传统MGM(1,m)模型,综合了两者各自的优势。系统的自忆性方程包含多个时次初始场而不仅是单个时次初始场,从而克服了传统灰色预测模型对初值比较敏感的弱点。对基坑变形预测的实例研究结果表明,所构建模型能够充分利用系统的多个历史时次资料,可以紧密捕捉系统演化趋势,模拟预测精度显著高于传统多变量MGM(1,m)模型。研究结果表明,新模型丰富和完善了灰色预测理论,值得推广应用于其他类似的多变量系统。     

信息技术项目产权结构及其管理模式的研究

基于Hart 产权理论, 应用两阶段博弈模型分析信息技术外包过程, 研究信息技术资产属 性、投资效用、人力资本属性对信息技术项目产权结构的影响. 公司根据资源重要性选择信息 技术外包的组织形式和产权结构, 控制被忽略的资产及其属性的负面作用, 特别是在外包过程 中应当充分考虑外包商人力资源管理和人力资源的稳定性. 证明了任何一方都应将投资用于 自己擅长的专业上, 任何信息技术项目产权结构都导致专用性投资不足, 产权结构选择是各种 产权结构的平衡, 关键问题是如何改进双边治理的低投资边际效益.     

Sufficient m-out-of-n (m/n) bootstrap

Traditional resampling methods for estimating sampling distributions sometimes fail, and alternative approaches are then needed. For example, if the classical central limit theorem does not hold and the naïve bootstrap fails, the m/n bootstrap, based on smaller-sized resamples, may be used as an alternative. An alternative to the naïve bootstrap, the sufficient bootstrap, which uses only the distinct observations in a bootstrap sample, is another recently proposed bootstrap approach that has been suggested to reduce the computational burden associated with bootstrapping. It works as long as naïve bootstrap does. However, if the naïve bootstrap fails, so will the sufficient bootstrap. In this paper, we propose combining the sufficient bootstrap with the m/n bootstrap in order to both regain consistent estimation of sampling distributions and to reduce the computational burden of the bootstrap. We obtain necessary and sufficient conditions for asymptotic normality of the proposed method, and propose new values for the resample size m. We compare the proposed method with the naïve bootstrap, the sufficient bootstrap, and the m/n bootstrap by simulation.     

Autocovariance Estimation in Regression with a Discontinuous Signal and m‐Dependent Errors: A Difference‐Based Approach

We discuss a class of difference‐based estimators for the autocovariance in nonparametric regression when the signal is discontinuous and the errors form a stationary m‐dependent process. These estimators circumvent the particularly challenging task of pre‐estimating such an unknown regression function. We provide finite‐sample expressions of their mean squared errors for piecewise constant signals and Gaussian errors. Based on this, we derive biased‐optimized estimates that do not depend on the unknown autocovariance structure. Notably, for positively correlated errors, that part of the variance of our estimators that depend on the signal is minimal as well. Further, we provide sufficient conditions for ‐consistency; this result is extended to piecewise Hölder regression with non‐Gaussian errors. We combine our biased‐optimized autocovariance estimates with a projection‐based approach and derive covariance matrix estimates, a method that is of independent interest. An R package, several simulations and an application to biophysical measurements complement this paper.